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  Subjects -> COMPUTER SCIENCE (Total: 2110 journals)
    - ANIMATION AND SIMULATION (31 journals)
    - ARTIFICIAL INTELLIGENCE (104 journals)
    - AUTOMATION AND ROBOTICS (102 journals)
    - COMPUTER ARCHITECTURE (10 journals)
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    - COMPUTER GAMES (22 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1241 journals)
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    - E-BUSINESS (22 journals)
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    - INFORMATION SYSTEMS (99 journals)
    - INTERNET (90 journals)
    - SOCIAL WEB (52 journals)
    - SOFTWARE (34 journals)
    - THEORY OF COMPUTING (9 journals)

COMPUTER SCIENCE (1241 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 26)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 31)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 9)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 17)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 4)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 9)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 11)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 16)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 8)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 6)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 21)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 37)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Informatica Malaysia     Open Access  
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 30)
Advanced Science Letters     Full-text available via subscription   (Followers: 12)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 9)
Advances in Artificial Intelligence     Open Access   (Followers: 16)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 6)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 20)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Computer Science : an International Journal     Open Access   (Followers: 17)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 58)
Advances in Engineering Software     Hybrid Journal   (Followers: 29)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 17)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 24)
Advances in Human-Computer Interaction     Open Access   (Followers: 21)
Advances in Materials Science     Open Access   (Followers: 17)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 53)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 6)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 10)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 5)
AI EDAM     Hybrid Journal   (Followers: 2)
Air, Soil & Water Research     Open Access   (Followers: 14)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 7)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 5)
American Journal of Information Systems     Open Access   (Followers: 7)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 8)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of Pure and Applied Logic     Open Access   (Followers: 4)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access   (Followers: 1)
Annual Reviews in Control     Hybrid Journal   (Followers: 8)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Applied Clinical Informatics     Hybrid Journal   (Followers: 3)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 14)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 12)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 17)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 7)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 6)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 165)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 7)
Artifact     Open Access   (Followers: 3)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access   (Followers: 2)
Asian Journal of Control     Hybrid Journal  
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 6)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 13)
Automation in Construction     Hybrid Journal   (Followers: 7)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Balkan Journal of Electrical and Computer Engineering     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Big Data and Cognitive Computing     Open Access   (Followers: 5)
Biodiversity Information Science and Standards     Open Access   (Followers: 1)
Bioinformatics     Hybrid Journal   (Followers: 361)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 38)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 50)
British Journal of Educational Technology     Hybrid Journal   (Followers: 187)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 13)
Bulletin of Social Informatics Theory and Application     Open Access  
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal   (Followers: 2)
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 15)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals : X     Open Access  
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 8)
China Communications     Full-text available via subscription   (Followers: 9)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Hybrid Journal   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Clinical eHealth     Open Access  
Cluster Computing     Hybrid Journal   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 3)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 15)
Communication Methods and Measures     Hybrid Journal   (Followers: 15)
Communication Theory     Hybrid Journal   (Followers: 25)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 3)
Communications in Information Science and Management Engineering     Open Access   (Followers: 4)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 4)
Communications of the ACM     Full-text available via subscription   (Followers: 57)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 4)
Computational and Mathematical Biophysics     Open Access   (Followers: 1)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 1)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 2)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 13)
Computational Chemistry     Open Access   (Followers: 3)
Computational Cognitive Science     Open Access   (Followers: 4)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 11)
Computational Economics     Hybrid Journal   (Followers: 10)
Computational Geosciences     Hybrid Journal   (Followers: 17)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 10)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 8)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access   (Followers: 1)
Computational Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Computer     Full-text available via subscription   (Followers: 114)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)

        1 2 3 4 5 6 7 | Last

Similar Journals
Journal Cover
Computer Communications
Journal Prestige (SJR): 0.459
Citation Impact (citeScore): 3
Number of Followers: 16  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0140-3664 - ISSN (Online) 0140-3664
Published by Elsevier Homepage  [3185 journals]
  • AdaWFPA: Adaptive Online Website Fingerprinting Attack for Tor Anonymous
           Network: A Stream-wise Paradigm
    • Abstract: Publication date: 15 December 2019Source: Computer Communications, Volume 148Author(s): Reyhane Attarian, Lida Abdi, Sattar Hashemi Nowadays, network traffic analysis is quite pervasive in human practice. Website fingerprinting attack, which is a new variant of traffic analysis attacks, identifies the websites visited by clients in encrypted and anonymized Tor connections by observing patterns in packet flows. Previous website fingerprinting attacks focus on static models in which the classifier is trained within a time period and then it is utilized to identify targeted websites. Static attacks cannot handle the time effect on the accuracy since their classifiers are not trained on the newest versions of the websites. Consequently, their accuracy drops drastically when tested on captured traffic traces of websites, days after training. This time effect is known as concept drift.In order to maintain the performance of the classifier, the classifier must be updated over time. In static attacks, updating the classifier includes updating the whole dataset again along with retraining the classifier. Recollecting, maintaining, and updating datasets as well as updating and retraining the classifier are very time and memory consuming. To deal with the emerging issues arising from concept drift and expensive retraining phases, this paper proposes AdaWFPA; an adaptive online website fingerprinting attack which is based on adaptive stream mining algorithms. AdaWFPA avoids concept drift by updating its model over time. Our empirical analyses and experiments over real world datasets indicate the superiority of our approach; as it offers better accuracy, precision, and recall in comparison with other state-of-the-art methods in the literature.
  • Pulse Position Coded Medium Access in energy-starved networks
    • Abstract: Publication date: 15 December 2019Source: Computer Communications, Volume 148Author(s): Dezhi Feng, Saptarshi Das, Faezeh Hajiaghajani, Yan Shi, Subir Biswas This paper presents a novel pulse position-coded PDU (PPCP) paradigm for multi-access wireless sensor nodes and IoT networks with thin energy budgets. The core idea is to encode a protocol data unit (PDU) in terms of the silence duration between two sets of delimiter pulses, whose positions are modulated based on the value of the PDU. This PPCP architecture achieves significant energy savings by using a lesser amount of bit/pulse transmissions, and by eliminating long multi-bit preambles and headers, which are normally used in traditional packets. The proposed multi-access pulse-based PDU scheme enables medium sharing among many sensor nodes without requiring per-PDU frame synchronization. Unlike previous Communication through Silence (CtS) literature, a full architecture design and prototype hardware platform of PPCP is presented here to demonstrate its advantages in terms of energy savings, reduced transmission delays, and ease of implementation. The advantages of PPCP compared to state-of-the-art Bluetooth Low Energy (BLE) and legacy binary packets are also shown. The prototype implementation shows reliable error detection based on a novel mechanism that relies on the inherent pulse-position modulation-based coding technique.
  • QoS enhancement with deep learning-based interference prediction in mobile
    • Abstract: Publication date: 15 December 2019Source: Computer Communications, Volume 148Author(s): Saniya Zafar, Sobia Jangsher, Ouns Bouachir, Moayad Aloqaily, Jalel Ben Othman With the acceleration in mobile broadband, wireless infrastructure plays a significant role in Internet-of-Things (IoT) to ensure ubiquitous connectivity in mobile environment, making mobile IoT (mIoT) as center of attraction. Usually intelligent systems are accomplished through mIoT which demands for the increased data traffic. To meet the ever-increasing demands of mobile users, integration of small cells is a promising solution. For mIoT, small cells provide enhanced Quality-of-Service (QoS) with improved data rates. In this paper, mIoT-small cell based network in vehicular environment focusing city bus transit system is presented. However, integrating small cells in vehicles for mIoT makes resource allocation challenging because of the dynamic interference present between small cells which may impact cellular coverage and capacity negatively. This article proposes Threshold Percentage Dependent Interference Graph (TPDIG) using Deep Learning-based resource allocation algorithm for city buses mounted with moving small cells (mSCs). Long–Short Term Memory (LSTM) based neural networks are considered to predict city buses locations for interference determination between mSCs. Comparative analysis of resource allocation using TPDIG, Time Interval Dependent Interference Graph (TIDIG), and Global Positioning System Dependent Interference Graph (GPSDIG) is presented in terms of Resource Block (RB) usage and average achievable data rate of mIoT-mSC network.
  • Generation of Frequent sensor epochs using efficient Parallel Distributed
           mining algorithm in large IOT
    • Abstract: Publication date: Available online 18 September 2019Source: Computer CommunicationsAuthor(s): R.M. Rani, M. Pushpalatha Numerous data mining algorithms are implemented using huge volume of sensor data to generate the frequent item sets that are useful in many aspects such as to predict the behavioural sensor patterns of future events and to detect the survival of sensors in large IOT . Traditional data mining algorithms transactional databases to produce the frequent patterns. Since the rate of input data arrival in large IOT varies, it becomes difficult to mine the dynamic sensor database. This work proposes an efficient algorithm known as Vertical Partitioning Parallel Distributed Algorithm(VPPDA) that uses MapReduce framework to mine sensor epochs to detect the survival of sensors using association rules formed by generated frequent patterns. The proposed VPPDA eliminates overhead of interprocess communication, by introducing overlapped window concept implemented in MapReduce framework combined with Vertical partitioning approach to get better progress in execution time and accurate results. Additionally pipelining processing has been implemented in MapReduce framework that still increases the performance and generates the frequent sensor epochs patterns. The concept of pipeline processing was introduced to minimize the execution time and also make the situation adaptable to any input rate of incoming sensor epochs.
  • An efficient architecture for the accurate detection and monitoring of an
           event through the sky
    • Abstract: Publication date: Available online 14 September 2019Source: Computer CommunicationsAuthor(s): Amit Sharma, Pradeep Kumar Singh, Ashutosh Sharma, Rajiv Kumar The detection of an event at its early stage is still an open challenge and in its development stage. This manuscript is an attempt to identify the possibilities of Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) in the applications of disaster management. Major of the applications of systems that utilizes both WSNs and UAVs are classified and architecture is proposed for detecting and monitoring the event at its early stage. The proposed system utilizes both sensor network and UAV network for the application of fire detection. The system is capable of sensing the environmental parameters in real time and possesses continuous monitoring until an event occurs. The main objective of this work is to develop such a system that serves accurate detection along with its confirmation through real time images from UAVs. The experimental simulation presents the accurate detection rate and the approach is adequate for early event detection. The improvements in early detection system significantly reduce the damage cause due to late detection of an event.
  • Cross centric intrusion detection system for secure routing over black
           hole attacks in MANETs
    • Abstract: Publication date: Available online 13 September 2019Source: Computer CommunicationsAuthor(s): N. Rajendran, P.K. Jawahar, R. Priyadarshini Due to the constraint of resources in Mobile Adhoc Network (MANET), usually mobile nodes do not gain perfect routing efficiency. This routing efficiency will be reduced by several numbers of data blockages and the communication time in the routing path. There are many data blockages arising, especially Black Hole Attack (BHA) is a massive anomalous attack in routing. This may cause the efficiency of communication time between nodes. In this juncture, the availability of Intrusion Detection System with Cross centric network which is overloaded with routing path packets, so it is easily affected by the BHA. To decrease the impact of black hole attack using Cross Centric Network Intrusion Detection System, this research proposes the CniDsor framework. CniDsor framework is a diminutive form of the “Cross Centric Intrusion Detection System for Secure Routing over Black Hole Attacks in MANETs”. This system is used to prevent the Black Hole Attacks over the Secure Routing. CniDsor works based on the Path Origin Selection, Priority Portion Assignment (PPA) and IDS attack reduction. In this study, path origin selection utilizes secure routing by “PIHNSPRA Routing Algorithm” and it selects the path from end to end nodes with secure manner and reduces the black-hole attack. Meanwhile, priority portion assignment is used to manage the node position and network monitoring by the “Past Interaction History”. Finally, CniDsor framework provides secure IDS communication efficiency in MANET which is used to accomplish the efficient routing path. Moreover, the proposed CniDsor framework works better and minimizes the communication overhead, packet delay and end to end delay, thus increases the network lifetime, packet delivery ratio (PDR).
  • Implementation of Fruit Fly Optimization Algorithm (FFOA) to escalate the
           attacking efficiency of node capture attack in Wireless Sensor Networks
    • Abstract: Publication date: Available online 12 September 2019Source: Computer CommunicationsAuthor(s): Ruby Bhatt, Priti Maheshwary, Piyush Shukla, Prashant Shukla, Manish Shrivastava, Soni Changlani Wireless sensor network (WSN) is a group of a huge number of low price, low control, and self-organizing specialized sensor nodes. WSN is very much vulnerable to different types of physical attacks due to limited resource capacity and screened to external atmosphere for circulating data. The node capture attack is one of the major attacks in WSN in which the intruder physically captures the node and remove the secret information from the node’s memory. We propose a Fruit Fly Optimization Algorithm (FFOA) that is based on multiple objectives node capture attack algorithm which consists of several objectives: maximum node contribution, maximum key contribution, and least resource expenses to discover optimal nodes. It will influence an inclusive tool to demolish maximum part of the network along with effective cost and maximum attacking efficiency. The simulation result illustrates that FFOA obtains a maximum fraction of compromised traffic, lower attacking rounds, and lower energy cost as compared with Genetic Algorithm (GA) and other node capture attack algorithms. Therefore, FFOA gives maximum attacking efficiency than GA and other algorithms by capturing minimum nodes that compromise the whole network.
  • openLEON: An end-to-end emulation platform from the edge data center to
           the mobile user
    • Abstract: Publication date: Available online 7 September 2019Source: Computer CommunicationsAuthor(s): Claudio Fiandrino, Alejandro Blanco Pizarro, Pablo Jiménez Mateo, Carlos Andrés Ramiro, Norbert Ludant, Joerg Widmer To support next generation services, 5G mobile network architectures are increasingly adopting emerging technlogies like software-defined networking (SDN) and network function virtualization (NFV). Core and radio access functionalities are virtualized and executed in edge data centers, in accordance with the Multi-Access Edge Computing (MEC) principle. While testbeds are an essential research tool for experimental evaluation in such environments, the landscape of data center and mobile network testbeds is fragmented. In this work, we aim at filling this gap by presenting openLEON, an open source muLti-access Edge cOmputiNg end-to-end emulator that operates from the edge data center to the mobile users. openLEON bridges the functionalities of existing emulators for data centers and mobile networks, i.e., Containernet and srsLTE, and makes it possible to evaluate and validate research ideas on all the components of an end-to-end mobile edge architecture.
  • CHPC: A complex semantic-based secured approach to heritage preservation
           and secure IoT-based museum processes
    • Abstract: Publication date: Available online 7 September 2019Source: Computer CommunicationsAuthor(s): Konev Anatoly, Khaydarova Rezeda, Lapaev Maxim, Luanye Feng, Long Hu, Min Chen, Bondarenko Igor Preservation and conservation of cultural heritage artifacts are a still of high priority. Regulation of microclimate parameters such as humidity, temperature, luminosity etc. has a great effect on heritage preservation. Due to development of new technologies museum systems face the challenge of minimizing human interaction. In this paper, we propose a CHPC (Cultural Heritage Preservation and Conservation) system for automated regulation of microclimate parameters and assisting museum staff in the issues of choosing right exhibition halls for the artifacts depending on materials of items for the reasons of different store conditions, based on Internet of Things (IoT) and Artificial Intelligence (AI), particularly Semantic Web technologies. Typical use-cases for the system are described as well as the ways to support them. Moreover, the proposed CHPC system has to be secured since when using up-to-date technologies new threats appear. An overview of new security issues is proposed. We suggest architecture for museum system and spot on an overview of security architectural bulkhead to provide security of semantics and wireless infrastructure. Operating principles of security system are provided as well. To verify our proposal, experiments and verification tests are conducted.
  • A channel hopping based defense method against primary user emulation
           attack in cognitive radio networks
    • Abstract: Publication date: Available online 5 September 2019Source: Computer CommunicationsAuthor(s): Arash Ahmadfard, Ali Jamshidi Cognitive radio is a promising technology for the problem of spectrum scarcity. Security issues of this network have attracted great attention in the recent years. Primary user emulation attack is a security threat which can significantly degrade the performance of cognitive networks. In this attack, an attacker emulates the signal of the primary users to prevent the cognitive network from utilization of the channels. In this paper, we study a channel hopping based defense method. In particular, a cognitive network is considered that is under attack by a single attacker. The attacker’s goal is to degrade the total throughput of the cognitive network as much as possible. However, the each cognitive user wants to maximize its own throughput. The interactions among the cognitive users and the attacker are modeled by a multi-player zero-sum game and an algorithm is proposed for obtaining the Nash equilibrium of the game. Simulation results are presented to clarify the performance of the algorithm.
  • Dynamic control of functional splits for energy harvesting virtual small
           cells: A distributed reinforcement learning approach
    • Abstract: Publication date: Available online 4 September 2019Source: Computer CommunicationsAuthor(s): Dagnachew Azene Temesgene, Marco Miozzo, Paolo Dini To meet the growing mobile data traffic demand, Mobile Network Operators (MNOs) are deploying dense infrastructures of small cells as a solution for capacity enhancement. This densification increases the power consumption of mobile networks, thus impacting the environment. As a result, we have seen a recent trend of powering base stations with ambient energy sources to achieve both environmental sustainability and cost reductions. In addition, flexible functional split in Cloud Radio Access Network (CRAN) is a promising solution to overcome the capacity and latency challenges in the fronthaul. In such architecture, local base stations perform partial baseband processing while the remaining part will take place at the central cloud. As the cells become smaller and deployed in a densified manner, it is evident that baseband processing power consumption has a huge share in the total base station power consumption breakdown. In this paper, we propose a network scenario where the baseband processes of the virtual small cells powered solely by energy harvesters and batteries can be opportunistically executed in a grid-connected edge computing server, co-located at the macro base station site. We state the corresponding energy minimization problem and propose multi-agent Reinforcement Learning (RL) to solve it. Distributed Fuzzy Q-Learning and Q-Learning on-line algorithms are tailored for our purposes. Coordination among the multiple agents is favored by broadcasting system level information to the independent learners. The evaluation of the network performance confirms that favoring coordination among the agents via broadcasting may achieve higher system level gains and cumulative rewards closer to the off-line bounds than solutions that are unaware of system level information. Finally, our analysis permits to evaluate the benefits of continuous state/action representation for the learning algorithms in terms of faster convergence, higher cumulative reward and adaptivity to changing environments.
  • A lightweight CLAS scheme with complete aggregation for healthcare mobile
    • Abstract: Publication date: Available online 4 September 2019Source: Computer CommunicationsAuthor(s): Ismaila Adeniyi Kamil, Sunday Oyinlola Ogundoyin The vast increase in the deployment of smart devices and the ubiquity of the Internet have evolved a new technology referred to as mobile crowdsensing (MCS). In healthcare MCS (HMCS), mobile devices can collect and upload medical data to the cloud server where authorized healthcare providers can access the relevant data for proper diagnosis and treatments. However, since this process is directly connected with patient’s sensitive health information, any unauthorized disclosure may have serious consequences on the patient’s wellbeing, hence privacy and security are serious issues in HMCS. To achieve data privacy and integrity, some certificateless aggregate signature (CLAS) schemes have been proposed. Very recently, four independent CLAS schemes were proposed for healthcare application. The authors claimed that their schemes were semantically secure in the security model. In this work, we analyze these schemes and find them to be insecure since there exists an adversary who can always forge a valid signature. Afterwards, we put forward a new CLAS scheme for HMCS application based on Elliptic Curve Cryptography (ECC) and hash function. In the proposed scheme, an aggregator can perform complete aggregation of certificateless signatures, resulting in improved performance. The proposed scheme satisfies all the security and privacy requirements of HMCS and can prevent possible attacks. Moreover, we show that the scheme is semantically secure with the assumption that the Discrete Logarithm Problem (DLP) is intractable. Extensive performance analysis and comparison show that the scheme is much more efficient than the state-of-the-art schemes.
  • An optimal sensor placement algorithm (O-SPA) for improving tracking
           precision of human activity in real-world healthcare systems
    • Abstract: Publication date: Available online 4 September 2019Source: Computer CommunicationsAuthor(s): Abdulaziz Alarifi, Ahmad Ali AlZubi, Mohammed Al-Maitah, Basil Al-Kasasbeh In this article, an Optimal Sensor Placement Algorithm (O-SPA) is designed for improving the precision of human activity. This placement algorithm is designed based on the fact of improving the Rate of Observation (RO) and sensing in the day-to-day activities of human. SPA for RO and sensing improves the analysis precision by accumulating and classifying useful sensor information. Useful sensor information is achieved by approximating the sensor data mitigating positioning errors. The analysis process differentiates useful and less preference information from the placed sensors to reduce complications in classification. This device positioning algorithm is more specific for the precision of data accumulation and manipulation by reducing sensing errors to improve the efficiency of healthcare systems The efficiency of healthcare systems with optimal sensor deployment is measured in terms of accuracy (98.9%), classification time (0.079s), replications (4.12), recall (96.29%), and error factor (0.000021). The efficiency results are the best compared to Hybrid sensing aided human activity recognition (HHAR), Multi-sensor fusion with ensemble pruning system (MSF-EP) and Activities of daily living Compressive sensing (ADL).
  • Equipping recurrent neural network with CNN-style attention mechanisms for
           sentiment analysis of network reviews
    • Abstract: Publication date: Available online 3 September 2019Source: Computer CommunicationsAuthor(s): Mohd Usama, Belal Ahmad, Jun Yang, Saqib Qamar, Parvez Ahmad, Yu Zhang, Jing Lv, Joze Guna Deep learning algorithms have achieved remarkable results in natural language processing (NLP) and computer vision. Especially, deep learning methods such as convolution and recurrent neural networks have shown remarkable performance in text analytic task. Moreover, from the attention mechanism perspective convolutional neural network (CNN) is applied less than recurrent neural network (RNN). Because RNN can learn long-term dependencies and gives better results than CNN. But CNN has its own advantage, can extract high-level features invariant to the local translation by using its local fix size context at the input level. Thus, in this paper, we proposed a new model based on RNN with CNN-style self-attention mechanism by using the merits of both architectures together in one model. In the proposed model, first, CNN learns the high-level representation of words at the input level. Second, we used self-attention mechanism to get the attention of the model on the features which contribute much in the prediction task by calculating the attentive context vectors over hidden states representation generated from CNN. Finally, hidden state representations from CNN with attentive context vectors are commonly used at the RNN to process them sequentially. To validate the model we experiment on three benchmark datasets i.e. Movie review, Stanford sentiment treebank1, and treebank2. Experiment results and their analysis demonstrate the effectiveness of the proposed model.
  • WOTPY: A framework for web of things applications
    • Abstract: Publication date: Available online 3 September 2019Source: Computer CommunicationsAuthor(s): Andrés García Mangas, Francisco José Suárez Alonso The interoperability problems that originate from the heterogeneity in protocols and platforms is one of the main challenges currently faced by the Internet of Things (IoT). The Web of Things (WoT) is an architectural solution to this issue based on leveraging the Web as a means to ensure interoperability. The World Wide Web Consortium (W3C) is currently behind one of the most relevant WoT initiatives—a group of building blocks to serve as a possible foundation for the WoT. This work describes an experimental framework based on the W3C WoT, including a set of concrete and original protocol binding implementations (HTTP, Websockets, MQTT and CoAP). One of the main novelties is that all protocol binding implementations have support for all interaction verbs from the WoT interaction model. The framework is especially adequate to build WoT applications for devices on all layers of the fog computing model; this multi-layer integration is achieved by leveraging the W3C WoT architecture and interaction model. A functional implementation in Python is also described, including low-level designs and implementation details for the binding templates. The behavior of the framework and the protocol bindings is studied by implementing a benchmark application under multiple conditions and hardware platforms. Finally, recommendations are extracted from the obtained results for the most adequate protocols for each scenario and interaction verb.
  • Opportunistic schedulers and asymptotic price for fairness
    • Abstract: Publication date: Available online 3 September 2019Source: Computer CommunicationsAuthor(s): Veeraruna Kavitha, N. Hemachandra, Mayur Zambre We consider the well-known wireless fair opportunistic schedulers (mainly the alpha-fair schedulers) and analyze their price of fairness (PoF). Efficient scheduler, designed from the system perspective, maximizes the sum of accumulated utilities of all the agents, accumulated over several time slots. On the other hand, the fair schedulers deviate from such a schedule to provide a given level of fairness to various customers utilizing the system. This obviously results in a lower (total) accumulated utility. We study this loss, using the well-known performance measure, the price of fairness. Previous studies show that the PoF mostly increases, as the number of agents increases. We have very different results for opportunistic schedulers. We group agents into finite classes, each class having identical utilities and QoS requirements (inspired by wireless cellular networks), to obtain the asymptotic PoF (APoF). This is always below one. Further, in many cases, the PoF actually decreases to zero/negligible value as the number of agents increases. We also consider the case of multiple resources with bounded utilities. We again have zero/small APoF, depending upon the distributions of the utilities and the relative proportions of various classes. We derive closed-form/easily computable expressions for the APoF, using extreme-value theory, center-order statistics and the maximum theorem.
  • Constrained pattern extension algorithm based peak power reduction
           techniques for MIMO-OFDM applications
    • Abstract: Publication date: Available online 3 September 2019Source: Computer CommunicationsAuthor(s): B. Ramesh, J. Senthilkumar, Y. Suresh, V. Mohanraj In any correspondence framework, the emphasis is on assessing the channel drive reaction to recover the transmitted information flag precisely at the recipient’s end. The Constrained Pattern Extension (CPE) plot gives successful methods for foreshorten the peak-to-average power ratio (PAPR) without the requirement for side data. In these proposed calculations, CPE is utilized to moderate MAI. In CPE, each sub channel is deteriorated utilizing particular disintegration and transmit precoding matrix. This other framework changes the spatial channel into a progression of parallel sub channels with no crosstalk from each other. CPE with ideal power and sub-carrier distribution is performed with the requirement of aggregate power which amplifies the base client limit and entirety limit fundamentally than the current calculations. CPE with versatile bit stacking and control allotment has been finished with the imperative of information rate which enhances the BER execution significantly than the current calculations. To accomplish great QoS, different clients are given need in relegating sub channels to that of various clients. MIMO clients are viewed as first to distribute energy to fulfill their information rate prerequisites and after that whatever is left of the power is dispersed among the sub channels of clients utilizing the versatile bit stacking calculation. Along these lines, the proposed calculation gives well least client limit, and entirety limit with low intricacy contrasted with existing asset portion calculations figure client SISO and MIMO-OFDM frameworks. At long last, two ACE calculations have been proposed in this theory to build the framework limit and BER execution with zero MAI. In this manner, these proposed calculations can be utilized in future remote correspondence frameworks for better and enhanced exhibitions.
  • A review of data sets of short-range wireless networks
    • Abstract: Publication date: Available online 30 August 2019Source: Computer CommunicationsAuthor(s): Zhiting Lin, Pengfei Wang With the rapid development of intelligent devices, it is possible to communicate among devices without base-stations. There is a growing body of research on short-range wireless networks. However, to our knowledge, currently there is little guidance on how to choose the appropriate real data sets to study a specific area of short-range wireless networks, or to verify the proposed algorithms for a specific application. Therefore, this study reviews several real data sets collected by short-range wireless communication devices, analyzes characteristics of each data set, and classifies articles using each data set into several categories. By tracking the latest progress in short-range wireless networks and investigating how to use the real data, this study not only provides guidance for researchers who need to select data sets for analysis or application, but also provides a reference for those who want to perform experiments to collect new traces.
  • Fuzzy based energy efficient workload management system for flash crowd
    • Abstract: Publication date: Available online 28 August 2019Source: Computer CommunicationsAuthor(s): Om Kumar C.U., Ponsy R.K. Sathia Bhama Many organizations are moving towards the cloud to meet sudden spikes due to a flash crowd. The exponential growth in requests and the offloading of the computations have increased data centers which in turn increases energy consumption and carbon footprint. This paper aims to propose an energy-efficient workload management scheme for the green cloud. The novelty of the work entails i) Developing a three-staged scheduling scheme that maps Virtual Machines with the active Physical Machines resulting in the consolidation of workload. ii) Deploying Fuzzy Decision Maker to analyze under-utilized and over-utilized servers based on the cores and the type of workload. The efficiency of the proposed system is tested through a trace-based simulation. The Power Usage Effectiveness benchmarked with standard algorithms claims that this work averagely conserves 1.9% energy by triggering 60% lesser migrations and reduces the execution time of cloud instances averagely by 6 clock cycles with a minimum Service Level Agreement Violations of 0.1%
  • MDS: Multi-level decision system for patient behavior analysis based on
           wearable device information
    • Abstract: Publication date: Available online 27 August 2019Source: Computer CommunicationsAuthor(s): Amr Tolba, Omar Said, Zafer Al-Makhadmeh Smart healthcare devices and applications are designed to rely on intelligent sensing devices and wireless communication networks. The purpose of this integration is to provide better patient monitoring and facilitate modest disease diagnosis. Wireless biomedical sensing devices are placed in the patient’s body for periodic and regular monitoring and updating of the sensed information. Based on this information, the behavior of the patient and nature of the disease were identified for use in further diagnosis and prediction. This manuscript introduces a multi-level decision system (MDS) for monitoring and detecting patient behavior based on sensed information. The information from the devices is matched with historical data to understand the current state of the patient’s health. The process of decision making on the basis of the received sensor information at the medical center, is invoked for granting medical recommendations to the patients. MDS stores new phenomena in each patient’s health for future evaluation and it determines the frequency for analysis based on the behavior of the patient. MDS is intended to reduce doctors’ analysis and recommendation time, even with minimal information. MDS also conducts flexible information analysis in order to match patient behavioral analysis, so as to produce better recommendations. Experimental analysis of MDS proves its reliability by improving accuracy, true positive rate, F-measure score, and by reducing fusion delay.
  • Improved EGC method for increasing detection in cognitive radio networks
    • Abstract: Publication date: Available online 26 August 2019Source: Computer CommunicationsAuthor(s): M.S. Sumi, R.S. Ganesh The detrimental effects of shadowing and fading, affect the reliability of single user spectrum sensing in Cognitive Radio Networks. Hence to overcome this issue, cooperative spectrum sensing is performed. Fusion rules play an important role in improving the consistency of cooperative spectrum sensing. In this work, in addition to analysing the different soft fusion rules, an improved Equal Gain Combining method (EGC) is proposed to obtain better detection performance than conventional methods. Here, the number of results reported to Fusion Center also get reduced without loss in detection performance, thereby paving the way for energy conservation. Also the proposed improved EGC method is further modified in to two different schemes such as improved EGC method with conditions 1 and 2 (IEGCC1 and IEGCC2) to address the trade-off between throughput and energy consumption. Simulations have been carried out in MATLAB to compare and justify the performance of the proposed method and the modified schemes along with the conventional ones. It is observed that there is about 44% increase in detection in the proposed improved EGC method than the conventional EGC method, while simulating 20 users with SNR range of -30 to -11 and 48% increase while simulating 10 users with SNR range of -30 to -21 for a global false alarm probability of value 0.1. Also IEGCC2 method is found to have increased detection performance and highest energy efficiency of all the compared methods with about 23.31% increase in detection than the improved EGC method at a false alarm probability of 0.1.
  • High-dimensional feature extraction of sea clutter and target signal for
           intelligent maritime monitoring network
    • Abstract: Publication date: Available online 21 August 2019Source: Computer CommunicationsAuthor(s): Liu Ningbo, Xu Yanan, Ding Hao, Xue Yonghua, Guan Jian As one of the source sensors of maritime intelligent traffic network, radar plays an important role in maritime monitoring and early warning. The number of features extracted by the traditional maritime radar target detection method in feature domain is small, and the sea clutter and target echo features are often not linearly separable, so the radar detection performance is seriously affected by sea clutter. To solve this problem, this paper combines the time-frequency domain processing method with the residual neural network, and uses the time-frequency transform method to improve the signal-to-clutter ratio (SCR) and the degree of difference between sea clutter and target echo. On this basis, the high-dimensional time-frequency spectrum features are extracted by using the residual neural network to improve the utilization rate of radar echo information, form a high-dimensional feature space of sea clutter and target echo that are non-linearly separable, and realize the binary classification of sea clutter and target echo. It is verified by the measured data of X-band radar that the proposed target detection method can extract the deep features of the time-frequency spectrum of radar echo, has a high classification accuracy even in the case of low SCR, and has the potential to detect weak targets in the background of strong sea clutter. In addition, the influence of different time-frequency transform methods and polarization modes on target detection performance is further analyzed. The comparative study shows that different time-frequency transform methods and polarization modes have little influence on the classification accuracy of the proposed method. In comparison, under the conditions of fractional Fourier transform and cross-polarization, the proposed method has higher classification accuracy.
  • Efficient utilization of elliptic curve cryptography in design of a
           three-factor authentication protocol for satellite communications
    • Abstract: Publication date: Available online 21 August 2019Source: Computer CommunicationsAuthor(s): Arezou Ostad-Sharif, Dariush Abbasinezhad-Mood, Morteza Nikooghadam Satellite communications are one of the significant methods of communications, which can be used in long distances and in conditions that the other means of communications cannot be operated. With the aid of the satellite communications, many services, such as video or voice calling, television, fax, Internet, and radio channels are prepared. So far, a number of authenticated key agreement protocols have been presented for satellite communications. However, they cannot totally provide the desired security requirements. For instance, in this paper, we show that a newly-published scheme does not resist the ephemeral secret leakage attack and does not provide perfect forward secrecy. Therefore, in order to address these security challenges, we suggest a three-factor elliptic curve cryptography based protocol that can withstand the well-known attacks with a comparable performance. To confirm the security of the proposed protocol, it has been formally analyzed using both automated validation of Internet security protocols and applications (AVISPA) tool and random oracle model. Furthermore, through a descriptive security analysis, we demonstrate that the presented scheme is safe and robust against various attacks. Eventually, to indicate the performance of the proposed protocol, a comparative efficiency analysis has been done beside an experimental evaluation on a suitable hardware. The results are indicative of the security and efficiency of the proposed protocol.
  • CCCLA: A cognitive approach for congestion control in Internet of Things
           using a game of learning automata
    • Abstract: Publication date: Available online 20 August 2019Source: Computer CommunicationsAuthor(s): Soulmaz Gheisari, Ehsan Tahavori Internet of Things (IoT) typically consists of lossy and low powered networks (LLN) of interconnected sensors. Due to low bandwidth and high scale of communication, congestion can occur among the sensor nodes in the LLN, during communicating to a border router, or when some other clients from the Internet access the resources in the LLN. So, having a proper congestion control mechanism is very important for IoT. In this paper we want to cope with congestion in IoT; however the current IoT, which is still based on traditional static architectures, lacks intelligence and cannot comply with the increasing application performance requirements. Adding cognition in IoT empowers it with a brain and high level intelligence. Therefore, firstly a learning automata-based cognitive framework has been proposed for integrating cognition into IoT. Then, based on the framework, we have presented a new cognitive approach for congestion control, named CCCLA (Cognitive Congestion Control in IoT using a game of Learning Automata). In the proposed approach, a team of LA has been assigned to a group of effective controllable parameters; for example parameters, whose values can affect the congestion control. Each automaton has a finite set of possible values of its corresponding parameter, and it tries to learn the best one, which maximize the whole network performance. Each node in the network has its own group of learning automata, which act independently; however, all nodes receive the same feedbacks from the environment. Using simulation, we test the proposed cognitive framework in a congestion control scenario. Based on our findings CCCLA significantly avoids congestion while improves desired QoS parameters such as delay, reliability and throughput, even in highly lossy networks.
  • A framework for distributed data mining heterogeneous classifier
    • Abstract: Publication date: Available online 20 August 2019Source: Computer CommunicationsAuthor(s): S. Urmela, M. Nandhini Distributed Data Mining (DDM) emerged as a huge area by the tremendous growth of geographically distributed data and powerful computational capability of computing. In this, ENcryption, NORMalization, MApping (ENORMA), a privacy preserving heterogeneous classifier framework for universal DDM is proposed. Three algorithms are proposed for maintaining data privacy, retrieval and integration on DDM. For data privacy, privacy-preserving algorithm is designed for protection of data in both the levels; for data retrieval, an algorithm is developed for value normalization and for integration, Mapping algorithm is developed to map the data with schema in global level. Experimental implementation on Electronic Health Records (EHRs), Job Recruitment Records (JRRs) and Agriculture Weather Forecast Records (AWFRs) datasets shows an improved result compared to conventional frameworks.
  • Efficient topview person detector using point based transformation and
           lookup table
    • Abstract: Publication date: Available online 19 August 2019Source: Computer CommunicationsAuthor(s): Imran Ahmed, Misbah Ahmad, Muhammad Nawaz, Khalid Haseeb, Sajidullah Khan, Gwanggil Jeon Nowadays due to big data revolution, image analytics is seen as a potential solution to solve different real-life problems. In this regard, one of the applications could be a person detection system. The overhead mounted camera with a wide-angle lens gives more coverage and visibility in occluded and cluttered environments than a traditional or frontal view. Person detection from the top view is a challenging task because there is variation in position, orientation, poses, body articulation and appearance of a person depending upon the position in the scene. To handle these issues an efficient method is proposed that uses different geometric transformations, concepts of perspective geometry and homography matrix as a pre-processing step. The composite transformation matrix is then used with perspective transformations to standardized the shape of the image patch containing person. At this stage, the histogram of oriented features is extracted along with our proposed five additional spatial features. These features are then fed to a linear SVM classifier for training and testing and finally, a simple effective clustering process is used to accumulate the votes of SVM to render a decision for localization of the Person in the image. To reduce the computational cost of these points based geometric and perspective transformations a Lookup table structure is used which contains pre-calculated positions of different perspective points against spatial co-ordinates. The use of lookup table and point based operations significantly reduced processing time up to 50% as compared to the previous approach which uses RHOG algorithm with image-based rotations, transformations, interpolations, and no lookup structure. The proposed method is efficient both in terms of computation and accuracy. The performance of the proposed algorithm is tested using our newly recorded dataset having more wide coverage of the scene. The performance of the developed technique shows an accuracy of 98% TDR with 3% FDR.
  • Theoretic derivations of scan detection operating on darknet traffic
    • Abstract: Publication date: Available online 19 August 2019Source: Computer CommunicationsAuthor(s): Morteza Safaei Pour, Elias Bou-Harb Cyber space continues to be threatened by various debilitating attacks. In this context, executing passive measurements by analyzing Internet-scale, one-way darknet traffic has proven to be an effective approach to shed light on Internet-wide maliciousness. While typically such measurements are solely conducted from the empirical perspective on already deployed darknet IP spaces using off-the-shelf Intrusion Detection Systems (IDS), their multidimensional theoretical foundations, relations and implications continue to be obscured. In this article, we take a first step towards comprehending the relation between attackers’ behaviors, the width of the darknet vantage points, the probability of detection and the minimum detection time. We perform stochastic modeling, derivation, validation, inter-correlation and analysis of such parameters to provide numerous insightful inferences, such as the most effective IDS and the most suitable darknet IP space, given various attackers’ activities in the presence of detection time/probability constraints. One of the outcomes suggests that the detection strategy employed by the widely-deployed Bro IDS is ideal for inferring slow, stealthy probing activities by leveraging passive measurements. Further, the results do not recommend deploying the strategy utilized by the Snort IDS when the available darknet IP space is relatively small, which is a typical scenario when darknets are operated and employed on organizational networks. In addition, we provide an optimization problem set that identifies a new botnet early infection strategy, which can be leveraged by evolving stealthy bots to circumvent a certain IDS strategy as it operates on the darknet IP space. The implications of this formal derivation are especially factual with the advent of evolving paradigms such as IPv6 deployments, and the proliferation of highly-distributed, orchestrated, large-scale and stealthy probing botnets.
  • On routing, spectrum and network coding assignment problem for transparent
           flex-grid optical networks with dedicated protection
    • Abstract: Publication date: Available online 16 August 2019Source: Computer CommunicationsAuthor(s): Dao Thanh Hai Network coding (NC) is a revolutionary technique that fundamentally changes the traditional operations of networks to achieve better performance thanks to the excellent feature of in-network data manipulation. In optical networks, the failure recovery problem presents a ripe environment for applying NC and indeed, NC-based protection has marked a major departure from traditional protection schemes as it could potentially challenge the well-established trade-off between recovery time and resource efficiency. As elastic optical networks (EONs) have been widely accepted as the paradigm for next-generation optical core networks and the all-optical network coding technologies have been progressing quickly and maturing, this confluence features great opportunities for leveraging the network efficiency. In taking advantage of such opportunities, this paper conceives a perspective of integrating the all-optical XOR network coding to the dedicated path protection in EONs to boost the spectrum utilization efficiency while retaining the merit of near-instantaneous recovery capability. The proposal on NC-backed dedicated protection scheme empowers the conventional 1+1 routing and spectrum allocation with a new dimension on network coding assignment for improving network performance. We thus introduce a new research problem, called 1+1 routing, spectrum and network coding assignment and formulate it as an integer linear programming model aiming at maximizing the network throughput under capacity-constrained conditions. Numerical evaluations on realistic topologies highlight the efficacy of our proposal with a remarkable performance improvement as more than 20% traffic, in average, could be further accommodated in comparison with the traditional 1+1 counterpart.
  • Collaborative spectrum sensing mechanism based on user incentive in
           cognitive radio networks
    • Abstract: Publication date: November 2019Source: Computer Communications, Volume 147Author(s): Masahiro Sasabe, Tomohiro Nishida, Shoji Kasahara In cognitive radio networks (CRNs), it is important for secondary users (SUs) to efficiently reuse spectrum without interfering communication of primary users (PUs). To acquire the communication opportunities, SUs first need become winning, i.e., suppressing its own miss detection probability under the upper limit imposed by PUs. Collaborative spectrum sensing (CSS) is a promising approach to improve the detection performance of SUs, where multiple SUs form a group and share their sensing results. In addition, the probability that winning SUs correctly detect idle state of PUs’ spectrum will affect their communication opportunities. We first formulate a global optimization problem as integer linear programming (ILP), which maximizes both the number of winning SUs and total communication opportunities among them. In CSS, we also have to consider the selfishness of SUs because winning SUs will compete with group members to acquire their own communication opportunities. To cope with this competitive problem in addition to scalability problem of the global optimization, we further formulate an individual optimization problem, which can be solved by a user-incentive based CSS mechanism composed of PU selection and group (re)formation among SUs, where communication opportunities are allocated to SUs according to their detection performance. Through simulation experiments, we show the proposed mechanism considering selfishness of SUs is competitive with the existing scheme based on group-level cooperation, in terms of both the ratio of winning SUs and total communication opportunities among them. Comparing with the global optimization, we also show that the proposed mechanism can support larger-scale systems with performance improvement. In addition, we show that the proposed mechanism can achieve stable group formation even under SUs’ selfish behavior. Finally, we discuss how the number of PUs affects the system performance.
  • DABFS: A robust routing protocol for warning messages dissemination in
    • Abstract: Publication date: Available online 14 August 2019Source: Computer CommunicationsAuthor(s): Shahab Haider, Ghulam Abbas, Ziaul Haq Abbas, Thar Baker Vehicular ad hoc networks play a pivotal role in the enrichment of transportation systems by making them intelligent and capable of avoiding road accidents. For transmission of warning messages, direction-based greedy protocols select the next hop based on the current location of relay nodes towards the destination node, which is an efficient approach for uni-directional traffic. However, such protocols experience performance degradation by neglecting the movement directions of nodes in bi-directional traffic where topological changes occur dynamically. This paper pioneers the use of movement direction and relative positions of source and destination nodes to cater to the dynamic nature of bi-directional highway environments for efficient and robust routing of warning messages. A novel routing protocol, namely, Direction Aware Best Forwarder Selection (DABFS), is presented in this paper. DABFS takes into account directions and relative positions of nodes, besides the distance parameter, to determine a nodes movement direction using Hamming distance and forwards warning messages through neighbor and best route discovery. Analytical and simulation results demonstrate that DABFS offers improved throughput and reduced packet loss rate and end-to-end delay, as compared with eminent routing protocols.
  • Crystal: A scalable and fault-tolerant Archimedean-based server-centric
           cloud data center network architecture
    • Abstract: Publication date: Available online 14 August 2019Source: Computer CommunicationsAuthor(s): Sara Nasirian, Farhad Faghani The explosive increase in adopting cloud services leads to the need for accommodating millions to even billions of servers in the supporting infrastructure of data center networks. Thus, providing highly scalable and noticeably fault-tolerant interconnecting architectures with great network capacity, limited delay, and reasonable energy consumption has become of paramount importance. In this paper, Crystal, a server-centric and recursively constructed structure, is presented. Crystal is made out of building blocks inspired by vertex configuration pattern of Archimedean solids and is intrinsically fault-tolerant mainly due to the adoption of multihoming mechanism in the building block. Crystal also scales double exponentially owing to its recursively defined architecture. Furthermore, to increase the routing efficiency and manage different kinds of failures in the network, two exclusively designed routing algorithms are proposed. Theoretical analysis and simulation results all witness that Crystal is highly fault-tolerant and can provide good network capacity and low latency to support delay-sensitive and data-intensive applications. Generally, Crystal satisfies a reasonable trade-off between all the major goals of designing a cloud data center network.
  • Software-defined forensic framework for malware disaster management in
           Internet of Thing devices for extreme surveillance
    • Abstract: Publication date: Available online 13 August 2019Source: Computer CommunicationsAuthor(s): Visu P., Lakshmanan L., Murugananthan V., Meenaloshini Vimal Cruz Malware perception is an important technique which has to be explored to analyze the corpus amount of malware in short duration for effective disaster management. Accurate analyses of malware must be done by detecting them in initial stage in an automatic way to avoid sever damage in Internet of Thing devices. This is enabled by visualizing malware by using a software-defined visual analytic system. Though many auto analysis techniques are present visualization of malware is one of the effective techniques preferred for large analysis. Malware exhibits malicious behavior on computing devices by installing harmful software such as viruses. The existing static and dynamic form of malware detection is an inefficient technique as it involve in disassembling of malicious code. In this project, the visualization of malware in the form of images is proposed in order to find the malicious insertion on the executable files of computing devices for extreme surveillance. The malware detection becomes easier to visualize the malicious behavior in form of images by feature based classification of images as the global property of exe gray scale image is unchanged. This will be an eye open in healing the security issues in cyber-crime and provide extreme surveillance.
  • Overlay Networking to ensure seamless communication in Underwater Wireless
           Sensor Networks
    • Abstract: Publication date: Available online 12 August 2019Source: Computer CommunicationsAuthor(s): A. Rehash Rushmi Pavitra, E. Srie Vidhya Janani Underwater Wireless Sensor Networks (UWSNs) finds its way actively in current researches where End-to-End communication is primarily focus with the notion of various expansion perspectives. Upgrading of machinery, equipments and sensors are involving for information and communication technologies. In general, data transmission from source to destination in underwater is asymmetric, i.e. transmitter and receiver section serves on distinct frequencies thereby complementing underwater communication. On the other hand, routing in UWSNs seems to be challenging this necessitates further exploration to complement underwater wireless communication in periodic manner. The research progressively needs to overcome certain limitations with Delay Tolerant Network (DTN) enabled JANUS (DTN-J) is an effective technique to ensure reliable data transmission which is crucial for underwater networks. A high efficiency microprocessor is widely been adopted in terms of decision making mechanism which complements for maximizing system performance. In this research, simulation results enhance the End-to-End communication and network lifetime together suggesting reliable tensor value which further assists to potential development in the field of UWSNs.
  • Malware traffic classification using principal component analysis and
           artificial neural network for extreme surveillance
    • Abstract: Publication date: Available online 9 August 2019Source: Computer CommunicationsAuthor(s): Arivudainambi D., Varun Kumar K.A., Sibi Chakkaravarthy S., Visu P. Code-driven systems have extent to more than half of the world’s populations in ambient data and connectivity, offering formerly unimagined opportunities and unexpected threats. Evolutions in Artificial Intelligence (AI) are seen increasing day by day especially in industrial builds. The unconventional technique of AI in cyber-attacks seems to be quite daunting. The idea of a machine growing its own knowledge through self-learning becomes sophisticated to attack things is a fretful problem to the cyber world. Most of the time, these AI enabled cyber-attacks are performed using advanced malwares which incorporates advanced evasion techniques to evade security perimeters. Traditional cyber security methods fail to cope with these attacks. In order to address these issues, robust traffic classification system using Principal Component Analysis (PCA) and Artificial Neural Network (ANN) is proposed for providing extreme surverillance. Further, these proposed method aims to expose various AI based cyber-attacks with their present-day impact, and their fortune in the future. Simulation is carried out using a self-developed autonomous agent which learns by itself. Experimental results confirm that the proposed schemes are efficient to classify the attack traffic with 99% of accuracy when compared to the state of the art methods.
  • Decentralized proof of location in vehicular Ad Hoc networks
    • Abstract: Publication date: Available online 9 August 2019Source: Computer CommunicationsAuthor(s): Felipe Boeira, Mikael Asplund, Marinho P. Barcellos Future cooperative transportation systems will be highly dependent on correct situation awareness that can be established with the help of inter-vehicular communication. Location information from surrounding vehicles will most likely be used in such systems to make automated driving decisions, making it essential to guarantee location assurance. In this paper we propose Vouch+, which provides a scheme to improve trustworthiness of shared location information. The proposed scheme uses cryptographic primitives and mobility awareness to enable location proofs that work also in high-speed scenarios. Vouch+ takes a decentralized approach to establish trust in location information, but can also be used with future 5G infrastructure. The evaluation of Vouch+ using a synthetic dataset from the city of Cologne shows that using a decentralized approach is viable for cases where traffic is dense enough. In addition, simulation-based experiments show that Vouch+ is able to handle the high-mobility environment of vehicular networks and can counteract studied position-based attacks using reaction strategies.
  • Crowd video event classification using convolution neural network
    • Abstract: Publication date: Available online 8 August 2019Source: Computer CommunicationsAuthor(s): S. Jothi Shri, S. Jothilakshmi Crowd Event Classification in videos is an important and challenging task in computer vision based systems. The Crowd Event Classification system recognizes a large number of video events. The decisive of the model is a difficult task in the event classification. The event classification model has generalization capability on works with a higher number of videos. The embodiment of Deep Learning in video event classification derives powerful and distinguishes feature portrayals. The features of events are extracted from raw data through massive videos with effective and efficient detection. The Convolutional Neural Network (CNN) has been established as a powerful classification model for event recognition problems. A higher quality of new dataset of 3000 frames collected from youtube videos belonging to four classes of crowd events namely Marriage, Cricket, Jallikkattu, and Shopping mall The system has used two Deep CNN infrastructures are namely baseline and VGG16, which detects predefined events and provides temporal evidence. The CNN Model automatically tests input video frames and detects the events of centrality at the video. The CNN extracts the video events features from the video input frames and distinguishes the events name correctly. The system shows more improved 100% results compare with each other models.
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
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