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  Subjects -> ELECTRONICS (Total: 207 journals)
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Annals of Telecommunications
Journal Prestige (SJR): 0.223
Citation Impact (citeScore): 1
Number of Followers: 7  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1958-9395 - ISSN (Online) 0003-4347
Published by Springer-Verlag Homepage  [2468 journals]
  • Attribute-based encryption of LSSS access structure with expressive
           dynamic attributes based on consortium blockchain

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      Abstract: Abstract Attribute-based encryption (ABE) allows users to encrypt and decrypt data based on attributes. It realizes fine-grained access control and can effectively solve the one-to-many encryption and decryption problem in open cloud application. Linear secret sharing scheme (LSSS) is the common access structure with a matrix on the attributes in ABE schemes, which may depict AND, OR, threshold operations, etc. However, LSSS access structure does not depict the complex and dynamic access policy of attributes, such as the complicated relationship of different attributes and the generation of dynamic attributes. It severely restricts the expansion of the practical application of ABE. Besides, there exists another problem; attribute authority (AA) in traditional ABE has a concentration of power and easily suffers from single-point failure or privacy leakage for being attacked or corrupted. Blockchain is a decentralized, tamper-free, traceable, and multi-party distributed database technology. Consortium blockchain (CB) is a partially centralized blockchain, whose openness is between the public blockchain and the private blockchain. In this paper, an ABE scheme on LSSS access structure with expressive dynamic attributes (EDA) based on CB (LSSS-EDA-ABE-CB) was proposed to resolve the above issues. EDA can construct the comprehensive attribute calculation expressions by conducting various operations, such as arithmetic operations, relational operations, and string operations. In virtue of the application of EDA, the proposed scheme can reconstruct new composite attributes to realize the dynamic adjustment of attributes. A partitioning method of EDA avoids one attribute appearing in two different EDA expressions. The CB technology enhanced the authority and trustworthiness of AA by openly recording AA’s attribute key distributions in CB transactions. The scheme in the paper was proven CPA-secure under the decision q-PBDHE assumption in standard model in the CB application environment. The scheme provides a more general data access policy and maintains the fine-grained character of ABE simultaneously. Finally, the security and performance analysis shows that the proposed scheme is secure and highly efficient.
      PubDate: 2023-05-18
       
  • Backscatter communication system efficiency with diffusing surfaces

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      Abstract: Abstract In an ambient backscatter communication system, the waves generated by a source are reflected by a tag, in a variable manner in time. Therefore, the tag can transmit a message to a reader, without generating any radio wave and without battery. As a consequence, such a communication system is a promising technology for ultra-low energy wireless communications. In the simplest implementation of such a system, the tag sends a binary message by oscillating between two states and the reader detects the bits by comparing the two distinct received powers. In this paper, for the first time, we propose to analyze the impact of the shape of diffusing flat panel surfaces that diffuse in all directions, on an ambient backscatter communication system. We establish the analytical closed form expression of the power contrast in the presence of flat panels, by considering a rectangular surface and a disk-shaped surface, and we show that diffusing surfaces improve the power contrast. Moreover, our approach allows us to express the contrast to noise ratio, and therefore to establish the BER performance. Furthermore, we show that it makes it possible to improve the energetic performance, thanks to diffusing surfaces. For any configuration characterized by a fixed source, tag and reader, we moreover determine the precise locations of diffusing surfaces, which induce a maximum efficiency of the surfaces, whatever the wavelength. Furthermore, we show that it becomes possible to easily determine an optimal frequency which maximizes the contrast power, thanks to the expression of the contrast power.
      PubDate: 2023-05-11
       
  • Social network malicious insider detection using time-based trust
           evaluation

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      Abstract: Abstract In recent years, malicious insider attacks have become a common fraudulent activity in which an attacker is often perceived as a trusted entity in Social Networks (SNs). At present, machine learning (ML) approaches are widely used to identify the behavior of users in the network. From this perspective, this paper presents an integrated approach, namely, Social network malicious insider detection (SID), which consists of long short-term memory (LSTM) and time-based trust evaluation (TBTE). The proposed SID aims to identify deviations in SN user behavior by monitoring their data. The proposed SID uses LSTM, an advanced version of the recurrent neural network (RNN), which precisely predicts the behavior of users and identifies the anomaly pattern in SNs. A time-based trust evaluation method is integrated with LSTM, which not only differentiates the abnormal behavior of SN users but also precisely categorizes an anomaly node as a malicious node, a new user or a broken node. Moreover, the proposed SID detects insiders accurately and reduces false alarms by providing a novel quantitative analysis for computing the balancing factor according to time, which avoids the misinterpretation of normal user patterns as anomalies. The performance of the proposed SID is evaluated in real time, which demonstrates that the detection accuracy for attacks is 96% for normal users and 98% for new users with a smaller time span.
      PubDate: 2023-04-24
       
  • A modular pipeline for enforcement of security properties at runtime

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      Abstract: Abstract Runtime enforcement ensures the respect of a user-specified security policy by a program by providing a valid replacement for any misbehaving sequence of events that may occur during that program’s execution. However, depending on the capabilities of the enforcement mechanism, multiple possible replacement sequences may be available, and the current literature is silent on the question of how to choose the optimal one. Furthermore, the current design of runtime monitors imposes a substantial burden on the designer, since the entirety of the monitoring task is accomplished by a monolithic construct, usually an automata-based model. In this paper, we propose a new modular model of enforcement monitors, in which the tasks of altering the execution, ensuring compliance with the security policy, and selecting the optimal replacement are split into three separate modules, which simplifies the creation of runtime monitors. We implement this approach by using the event stream processor BeepBeep and a use case is presented. Experimental evaluation shows that our proposed framework can dynamically select an adequate enforcement actions at runtime, without the need to manually define an enforcement monitor.
      PubDate: 2023-04-17
       
  • Analysis of SNR penalty in coherent WDM receiver system for detection of
           QPSK signal with component crosstalk

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      Abstract: Abstract We propose analytical modelling of component crosstalk on the performance of wavelength division multiplexed (WDM) receiver with generalized quadrature phase shift keying (QPSK) signal and coherent detection. A comprehensive study on the signal-to-noise ratio (SNR) penalty is conducted, which can be used to balance SNR values and the system's power budget in the presence of finite crosstalk sources. Results express that, in the presence of five crosstalk interferers, the crosstalk level leading to 1-dB of SNR penalty must be less than -23.8 to -26.5 dB for bit error rate (BER) from 10–7 to 10–13. For the BER of 10–9, the QPSK signal has a component crosstalk tolerance of -21.7 dB for a 1 dB SNR penalty with a single interferer. Furthermore, the study of spectral efficiency reveals that crosstalk level, SNR, and the number of active interferers perform a vital role in determining the bandwidth efficiency of the system. The analysis exploits the characteristic function method and Maclaurin series expansion to compute a closed form expression of BER over additive white Gaussian noise (AWGN) channel. Following the analysis, the SNR and bandwidth expenses of the system are examined numerically through the estimated BER and binary entropy function. The estimated values of the BER using the proposed model are in close agreement with a similar theoretical investigation for a single interferer.
      PubDate: 2023-04-03
       
  • Forensic investigation of Cisco WebEx desktop client, web, and Android
           smartphone applications

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      Abstract: Abstract Digital forensic analysis of videoconferencing applications has received considerable attention recently, owing to the wider adoption and diffusion of such applications following the recent COVID-19 pandemic. In this contribution, we present a detailed forensic analysis of Cisco WebEx which is among the top three videoconferencing applications available today. More precisely, we present the results of the forensic investigation of Cisco WebEx desktop client, web, and Android smartphone applications. We focus on three digital forensic areas, namely memory, disk space, and network forensics. From the extracted artifacts, it is evident that valuable user data can be retrieved from different data localities. These include user credentials, emails, user IDs, profile photos, chat messages, shared media, meeting information including meeting passwords, contacts, Advanced Encryption Standard (AES) keys, keyword searches, timestamps, and call logs. We develop a memory parsing tool for Cisco WebEx based on the extracted artifacts. Additionally, we identify anti-forensic artifacts such as deleted chat messages. Although network communications are encrypted, we successfully retrieve useful artifacts such as IPs of server domains and host devices along with message/event timestamps.
      PubDate: 2023-04-01
       
  • Mitigation of a poisoning attack in federated learning by using historical
           distance detection

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      Abstract: Abstract Federated learning provides a way to achieve joint model training while keeping the data of every party stored locally, and it protects the data privacy of all participants in cooperative training. However, there are availability and integrity threats in federated learning, as malicious parties may pretend to be benign ones to interfere with the global model. In this paper, we consider a federated learning scenario with one center server and multiple clients, where malicious clients launch poisoning attacks. We explore the statistical relationship of Euclidean distance among models, including benign versus benign models and malicious versus benign models. Then, we design a defense method based on our findings and inspired by evolutionary clustering. The center server uses this defense scheme to screen possible malicious clients and mitigate their attacks. Our mitigation scheme refers to the detection results of both the current and previous rounds. Moreover, we improve our scheme to apply it to a privacy threat scenario. Finally, we demonstrate the effectiveness of our scheme through experiments in several different scenarios.
      PubDate: 2023-04-01
       
  • A performance evaluation of C4M consensus algorithm

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      Abstract: Abstract Blockchain designed for Mobile Ad hoc Networks (MANETs) and mesh networks is an emerging research topic that has to cope with the network partition problem. However, existing consensus algorithms used in blockchain have been designed to work in a fully connected network with reliable communication. As this assumption does not hold anymore in mobile wireless networks, we describe in this paper the problem of network partitions and their impact on blockchain. Then, we propose a new consensus algorithm called Consensus for Mesh (C4M) which is inspired by RAFT as a solution to this problem. The C4M consensus algorithm is integrated with Blockgraph, a blockchain solution for MANET and mesh networks. We implemented our solution in NS-3 to analyze its performances through simulations. The simulation results gave the first characterization of our algorithm, its performance, and its limits, especially in case of topology changes.
      PubDate: 2023-04-01
       
  • Finding gadgets in incremental code updates for return-oriented
           programming attacks on resource-constrained devices

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      Abstract: Abstract Code-reuse attacks pose a threat to embedded devices since they are able to defeat common security defences such as non-executable stacks. To succeed in his code-reuse attack, the attacker has to gain knowledge of some or all of the instructions of the target firmware/software. In case of a bare metal firmware that is protected from being dumped out of a device, it is hard to know the running instructions of the target firmware. This consequently makes code-reuse attacks more difficult to achieve. This paper presents a novel approach how an attacker can gain knowledge of some of these instructions by sniffing unencrypted incremental updates. These updates exist to reduce the radio reception power for resource-constrained devices. It will be demonstrated how a return-oriented programming (ROP) attack can be accomplished on a MSP430 MCU using only the passively sniffed incremental updates. The generated updates of the R3diff and Delta Generator (DG) differencing algorithms will be under assessment. The evaluation reveals that both of them can be exploited by the attacker and how an attacker can maximize his information gain when dealing with more than one update. It also shows that the DG generated updates leak more information than the R3diff generated updates. This stresses the fact that even delta updates need to be protected with encryption. To defend against this attack, different countermeasures that consider different power consumption scenarios are proposed, but yet to be evaluated.
      PubDate: 2023-04-01
       
  • Multipath neural networks for anomaly detection in cyber-physical systems

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      Abstract: Abstract An Intrusion Detection System (IDS) is a core element for securing critical systems. An IDS can use signatures of known attacks, or an anomaly detection model for detecting unknown attacks. Attacking an IDS is often the entry point of an attack against a critical system. Consequently, the security of IDSs themselves is imperative. To secure model-based IDSs, we propose a method to authenticate the anomaly detection model. The anomaly detection model is an autoencoder for which we only have access to input-output pairs. Inputs consist of time windows of values from sensors and actuators of an Industrial Control System. Our method is based on a multipath Neural Network (NN) classifier, a newly proposed deep learning technique for which we provide an in-depth description. The idea is to characterize errors of an IDS’s autoencoder by using a multipath NN’s confidence measure c. We use the Wilcoxon-Mann-Whitney (WMW) test to detect a change in the distribution of the summary variable c, indicating that the autoencoder is not working properly. We compare our method to two baselines. They consist in using other summary variables for the WMW test. We assess the performance of these three methods using simulated data. Among others, our analysis shows that: 1) both baselines are oblivious to some autoencoder spoofing attacks while 2) the WMW test on a multipath NN’s confidence measure enables detecting eventually any autoencoder spoofing attack.
      PubDate: 2023-04-01
       
  • Communication-friendly threshold trapdoor function from weaker assumption
           for distributed cryptography

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      Abstract: Abstract The Internet of things (IoT) market is booming with emerging applications in the automation process, transportation, customer transactions, industries, and healthcare by utilizing various IoT devices and sensors. IoT adoption helps organizations and companies to avoid high labour costs and improve their services. Also, people are gaining full control over their lives by using IoT devices like smart wearables, smart vehicles, laptops, tablets, and iPhones. However, devices cannot fully protect the secret data of the users. Localization of confidential information may be leaked due to a single server failure. Distributed cryptography plays a significant role to avoid a single point of failure by distributing a cryptographic operation among several servers instead of depending on a single server. In particular, threshold cryptography has the power to perform any cryptographic operation securely despite the compromise of a certain subset of servers. The Threshold trapdoor function (TTDF) is a thresholdized version of the Trapdoor function (TDF), an important base primitive in cryptography. TTDF facilitates sharing of the master trapdoor key among multiple servers so that at least a threshold number of servers jointly can invert the evaluated value of a randomly chosen function from the collection of TTDF. There are constructions of TTDF from the decisional Diffie-Hellman (DDH) and the learning with errors (LWE) assumptions which are strong as compared to the computational Diffie-Hellman (CDH) assumption. It is crucial to realize TTDF from a weaker hardness assumption. In this work, we provide the first TTDF construction under the hardness of the CDH problem by integrating Shamir’s threshold secret sharing with the CDH-based recyclable one-way function with encryption (OWFE) of Garg and Hajiabadi (8). Motivated by the concept of Garg et al. for building TDF using recyclableOWFE, we share the master trapdoor key by Shamir’s threshold secret sharing and provide a shared trapdoor key to each server so that each server can compute an inversion share of the image of a domain element. At least a threshold number of servers jointly can invert the image to recover the preimage. But, fewer than a threshold number of servers jointly are not able to invert the image. Our proposed TTDF achieves one-wayness despite the compromise of a certain subset of servers. Our security proof is in the standard model against the selective adversary. Our proposal yields a shorter image size as compared to the existing DDH-based TTDF scheme of Tu et al. (IET Inf Secur 14(2):220–231, 2019). Moreover, in comparison to the previous TTDF, our scheme performs better regarding communication bandwidth.
      PubDate: 2023-04-01
       
  • Cloud service selection in IoFT-enabled Multi-access Edge Computing: a
           Game Theoretic approach

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      Abstract: Abstract Nowadays, Multi-access Edge Computing (MEC) and Internet of Flying Things (IoFT) clouds are attracting significant attention from both academic and industrial research sectors as a new paradigm for providing flexible and elastic services. This new paradigm utilizes the recent technologies of Cloud Computing (CC) and Internet of things (IoT). The conjunctive application of these new technologies engenders an impressive vision of the future in which everything is connected to the Internet via Fifth Generation (5G) technology that integrates the MEC server; this will provide instant computing applications with low latency and fast service response. In the present study, we propose a Game Theory approach for Cloud Services in MEC- and UAV-enabled networks (GTCS) that enables a normal end user to select the most suitable Unmanned Aerial Vehicle (UAV)-Service-Provider based on a set of specific features, limitations, and prices. Given that every service provider has specific features, limitations, and prices, users must select the most suitable provider. A technique based on Game Theory (GT) is used to select the optimum provider by considering user requirements and UAV provider qualities. Simulation results obtained using the OMNet++ simulator and Inet framework evidence the efficiency and good performance of our method, which presents a low latency of 0.45 s at maximum between the two scenarios, a high successful execution ratio that reaches 100% with 50 Unmanned Aerial Vehicles (UAVs), and good management of energy consumption with only 14.5% energy loss in the worst case.
      PubDate: 2023-03-24
       
  • Energy-efficient Data Processing Protocol in edge-based IoT networks

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      Abstract: Abstract Wireless sensor networks (WSNs) represent an essential element of many applications in the Internet of Things (IoT) network and smart cities in the present and future. The sensor devices in these WSNs gather a lot of data, which will be sent to the edge gateway periodically. This would deplete the devices’ limited battery power and degrade the network’s performance. Therefore, it is important to turn off the redundant sensors that transmit the same data to the gateway and activate the minimum number of sensor nodes in the IoT network. This reduces the redundant sensed readings and decreases the overhead of communications, thereby extending the WSN’s lifetime. In this paper, an energy-efficient data processing (EDaP) protocol for edge-based IoT networks is proposed. The proposed EDaP protocol is implemented at two levels: sensor devices and the edge gateway. The data is collected by sensor devices and then encoded using either Huffman Encoding (HE) or the proposed Modified Run Length Encoding (MRLE). At the edge gateway level, the sensor node scheduling algorithm is implemented by the EDaP protocol to produce the best sensor schedule to fulfill the monitoring mission in the next period. The sensor nodes are scheduled based on the spatial correlation between their collected data using clustering methods. The simulation results are conducted to prove the effectiveness of the proposed technique, where it provides competitive results in comparison with some other work in terms of energy consumption, active sensor ratio, transmitted data ratio, and percentage of lost data.
      PubDate: 2023-03-16
      DOI: 10.1007/s12243-023-00957-8
       
  • Cybersecurity in networking: adaptations, investigation, attacks, and
           countermeasures

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      PubDate: 2023-03-14
      DOI: 10.1007/s12243-023-00956-9
       
  • Towards adversarial realism and robust learning for IoT intrusion
           detection and classification

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      Abstract: Abstract The internet of things (IoT) faces tremendous security challenges. Machine learning models can be used to tackle the growing number of cyber-attack variations targeting IoT systems, but the increasing threat posed by adversarial attacks restates the need for reliable defense strategies. This work describes the types of constraints required for a realistic adversarial cyber-attack example and proposes a methodology for a trustworthy adversarial robustness analysis with a realistic adversarial evasion attack vector. The proposed methodology was used to evaluate three supervised algorithms, random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM), and one unsupervised algorithm, isolation forest (IFOR). Constrained adversarial examples were generated with the adaptative perturbation pattern method (A2PM), and evasion attacks were performed against models created with regular and adversarial training. Even though RF was the least affected in binary classification, XGB consistently achieved the highest accuracy in multi-class classification. The obtained results evidence the inherent susceptibility of tree-based algorithms and ensembles to adversarial evasion attacks and demonstrate the benefits of adversarial training and a security-by-design approach for a more robust IoT network intrusion detection and cyber-attack classification.
      PubDate: 2023-03-11
      DOI: 10.1007/s12243-023-00953-y
       
  • Energy and delay trade-offs of end-to-end vehicular communications using a
           hyperfractal urban modelling

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      Abstract: Abstract We characterise trade-offs between the end-to-end communication delay and the energy in urban vehicular communications with infrastructure assistance. Our study exploits the self-similarity of the location of communication entities in cities by modelling them with a hyperfractal model which characterises the distribution of mobile nodes and relay nodes by a fractal dimension dF and dr, both larger than the dimension of the embedded map. We compute theoretical bounds for the end-to-end communication hop count considering two different energy-minimising goals: either total accumulated energy or maximum energy per node. Let δ > 1 be the attenuation factor in the street, we prove that when we aim to a total energy cost of order n(1−δ)(1−α), the hop count for an end-to-end transmission is of order \(n^{1-\alpha /(d_{F}-1)}\) , with α < 1 is a tunable parameter. This proves that for both goals, the energy decreases as we allow choosing routing paths of higher length. The asymptotic limit of the energy becomes significantly small when the number of nodes becomes asymptotically large. A lower bound on the network throughput capacity with constraints on path energy is also given. We show that our model fits real deployments where open data sets are available. The results are confirmed through simulations using different fractal dimensions in a Matlab simulator.
      PubDate: 2023-02-27
      DOI: 10.1007/s12243-022-00939-2
       
  • Secure and low PAPR OFDM system using TCCM

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      Abstract: Abstract Rapid communication uses orthogonal frequency division multiplexing (OFDM) to transfer multimedia data. OFDM combats frequency-selective fading and increases bandwidth efficiency. With many subcarriers, OFDM suffers from an elevated peak-to-average power ratio (PAPR), which hinders the potential of OFDM. The nonlinearity in the transmitted waveform is the result of high PAPR. This study implements compressed sensing (CS) to reduce PAPR because the OFDM signal is sparse in its frequency domain. Thus, the transmitter multiplies a well-designed topologically conjugate chaotic circulant matrix (TCCM), and the receiver end uses orthogonal matching pursuit (OMP). The TCCM involves a considerable selection of topologically conjugate chaotic functions. Chaotic matrices are preferred because they provide secure data transmission, and any minor change in the chaotic parameters results in irrecoverable data. The suggested chaotic system is validated using the bifurcation diagram (BD), Lyapunov exponent (LE), etc. This structured matrix reduces the PAPR considerably from 13 dB to below 7.5 dB, and its evaluation metric is the CCDF (complementary cumulative distribution function). Also, the investigation of the OFDM system involves image transmission, and the comparison is completed with a Gaussian matrix (GM), producing an improved peak-signal-to-noise ratio (PSNR) and bit error rate (BER) with reduced PAPR. This technique secures the data and reduces the PAPR, making it suitable for all future networks including cognitive and 5G networks.
      PubDate: 2023-02-16
      DOI: 10.1007/s12243-023-00948-9
       
  • Efficient approaches to optimize energy consumption in 3D wireless video
           sensor network under the coverage and connectivity constraints

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      Abstract: Abstract Two advanced approaches (PA_1, and PA_2) based on a realistic 3D model of video sensor nodes (VSNs) deployed randomly over a 2D target area are proposed to minimize energy consumption in the network maintaining area coverage and connectivity. The reduction of the number of active VSNs decreases energy consumption but at the same time reduces the area coverage and connectivity. These conflicting issues are resolved and an optimal solution is obtained by using an integer linear programming-based approach (PA_1). But PA_1 is not tractable for large instances as the problem is NP-Hard. Hence, a heuristic approach (PA_2) based on an advanced genetic algorithm is also proposed in the present work for obtaining a near-optimal solution. Simulation studies are carried out to compare the performance of PA_1 and PA_2 with the other three state-of-the-art approaches (APP_5, APP_6, and ET_3). Among the three existing approaches, APP_6/(ET_3) is the best in energy consumption/(area coverage). It is observed that for the same simulation environment, both PA_1 and PA_2 guarantee higher network services, by reducing energy consumption by 40.85% and 33.34% respectively compared to the best existing approach APP_6; and as well as by increasing area coverage by 0.94% than the best existing approach ET_3 for the node density 150 on the target area of size 75x75 square meter. Between PA_1 and PA_2, PA_2 generates a suboptimal solution and PA_1 substantiates its superiority by reducing energy consumption by 11.26% than PA_2 without losing area coverage for the same simulation environment.
      PubDate: 2023-02-11
      DOI: 10.1007/s12243-023-00947-w
       
  • Effects of dataset attacks on machine learning models in e-health

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      Abstract: Abstract E-health is a modern technology produced with the evolution and amalgamation of modern technologies such as the Internet of things (IoT) and machine learning (ML). The exploitation of efficient and suitable ML techniques to obtain appropriate data can enhance the mechanism of detection and ultimately prevent diseases. However, the datasets available in repositories for computerized medical analysis are inappropriate, incomplete, and prone to alteration and attacks. In this work, we consider attacks such as poison and evasion and analyze their effect on the decision-making processes in e-health. The results illustrate that the performance of the original model is high in almost all cases compared to the accuracy attained by the combined poisoned model. Interestingly, although the performance of the original model is higher, the difference is not that significant. For example, the artificial neural network achieves an accuracy of 75.39% on the original set. On the poisoned set, the artificial neural network achieves an accuracy of 74.5%. This means that the overall difference is just 1%. A similar trend can be found with the other classifiers except for the SVM and the logistic regression, where the difference is comparatively high. As such, our research proves that the protection of data in the training and testing phase is comparatively more important than the selection and application of the best ML technique.
      PubDate: 2023-02-10
      DOI: 10.1007/s12243-023-00951-0
       
  • Multi-carrier multi-level DCSK communication system based on time-reversal

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      Abstract: Abstract In order to overcome the shortcomings of low transmission rate in multi-user orthogonal multi-level DCSK (MOM-DCSK) communication system, a novel DCSK-based communication system is proposed. The new system is based on the time-reversal technology, M-ary and quadrature modulation. In this paper, K bits data are mapped to multi-transmission coefficients, distinguished by different Walsh codes and then transmitted on N in-phase and orthogonal carriers. The introduction of time-reversal architecture brings additional benefits such as doubling of band utilization and transmission rates. At the receiver, the received signals are input into moving average filters, in which the added noise variance is greatly reduced, thus reducing the system bit error rate (BER). Theoretical BER under additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels are derived and simulated, which are consistent with the numerical simulation results and prove the correctness of the formula. Compared with various systems, the results show that the proposed system has higher transmission rate and lower BER, which proves the rationality and superiority of the system as well as the feasibility of practical engineering applications.
      PubDate: 2023-01-30
      DOI: 10.1007/s12243-022-00942-7
       
 
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