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  Subjects -> ELECTRONICS (Total: 207 journals)
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Security and Communication Networks
Journal Prestige (SJR): 0.285
Citation Impact (citeScore): 1
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-0114 - ISSN (Online) 1939-0122
Published by Hindawi Homepage  [339 journals]
  • An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain
           in Hybrid Cloud Communication System

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      Abstract: The smart manufacturing system can become a linked network with the help of the Internet of Things (IoT). Devices connected to the IoT are susceptible to various attacks and assaults. An effective protection plan is needed to ensure that the billions of IoT nodes are protected from these hazards. The security mechanisms on IoT devices are ineffective due to resource limitations. As a result, the academic community has recently paid attention to the cloud-, fog-, and edge-based IoT systems. A robust cloud provider is in the cloud or fog to perform computationally demanding activities, including safety, data analysis, decision-making process, and monitoring. Hash identities and upgraded Rivest–Shamir–Adleman (RSA) have been used to secure the IoT device’s data. A four-prime integer of 512 bits makes up the proposed security algorithm. A hash signature is used to provide device authentication. An effective clustering method for sensing devices based on the node level, separation from the clusters, remaining energy, and fitness has been presented for long network life. The suggested swarm-based method determines the sensor nodes’ fitness. A deep neural network- (DNN-) based resource scheduling algorithm (DNN-RSM) is meant to reduce the delay and communications overhead for IoT components in the hybrid cloud system. For optimum resource allocation, all queries originating from the cluster head are categorised using DNN based on their storage, processing, and bandwidth needs. The suggested structure delivers better outcomes, particularly regarding energy use, delay, and safety level. The results of the simulation provide credence to the concept that the proposed strategy is superior to the current system. The suggested scheme includes stringent security, decreased energy usage, decreased latency, and efficient resource utilization.
      PubDate: Thu, 01 Jun 2023 07:50:01 +000
       
  • A Secure and Efficient Multi-Object Grasping Detection Approach for
           Robotic Arms

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      Abstract: Robot grasping is one of the most important abilities of modern intelligent robots, especially industrial robots. However, most of the existing robot arm’s grasp detection work is highly dependent on their edge computing ability, and the safety problems in the process of grasp detection are not considered enough. In this paper, we propose a new robotic arm grasping detection model with an edge-cloud collaboration method. With the scheme of multi-object multi-grasp, our model improves the mission success ratio of grasping. The model can not only complete the compression of full-resolution images but also achieve image compression at a limited bit rate. The image compression ratio reaches 2.03%; the structural difference value is higher than 0.91, and our average detection speed reaches 13.62 fps. Furthermore, we have packaged our model as a functional package of the ROS operating system, which can be easily used in actual robotic arm operations. Our solution can be fully applied to other work of robots to promote the development of the field of robotics.
      PubDate: Thu, 01 Jun 2023 06:35:00 +000
       
  • An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

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      Abstract: Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. Cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today’s network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model’s validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall.
      PubDate: Tue, 30 May 2023 04:35:00 +000
       
  • IoT Security Detection Method Based on Multifeature and Multineural
           Network Fusion

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      Abstract: IoT security detection plays an important role in securing the IoT ecosystem. The current detection systems suffer from poor fault tolerance and inefficient detection results. To address the IoT security vulnerability, the paper designs a multifeature fusion-based IoT security detection model to simulate an attacker sending test commands to IoT nodes. Firstly, the data collection algorithm is introduced, and the collected dataset is analyzed by three neural network models, namely, RNN, LSTM, and GRU, respectively. The best scoring model is selected as the classifier to identify vulnerabilities and achieve IoT security detection.
      PubDate: Mon, 29 May 2023 01:35:00 +000
       
  • Container Scaling Strategy Based on Reinforcement Learning

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      Abstract: Elasticity capability is one of the most important capabilities of cloud computing, which combines large-scale resource allocation capability to quickly achieve minute-level resource demand provisioning to meet the elasticity requirements of different scale scenarios. The elasticity capability is mainly determined by the container start-up speed and container scaling strategy together, where the container scaling strategy contains both vertical container scaling strategy and horizontal container scaling strategy. In order to make the container scaling policy more effective and improve the application service quality and resource utilization, we briefly introduce Kubernetes’ horizontal pod autoscaling (HPA) strategy, analyze the existing problem of HPA, and develop a container scaling strategy based on reinforcement learning. First, we analyze the problems of Kubernetes’ existing HPA container autoscaling strategy in the scale-up and scale-down phases, respectively. Second, the Markov decision model is used to model the container scaling problem. Then, we propose a model-based reinforcement learning algorithm to solve the container scaling problem. Finally, we compare the experimental results of the HPA scaling strategy and the model-based reinforcement learning strategy with the results from the resource utilization of the application, the change of the number of pods, and the application response time; through the experimental analysis, we verify that the reinforcement learning-based container scaling strategy can guarantee the application service quality and improve the utilization of the application resources more effectively than the HPA strategy.
      PubDate: Fri, 26 May 2023 06:35:00 +000
       
  • Blockchain for Credibility in Educational Development: Key Technology,
           Application Potential, and Performance Evaluation

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      Abstract: Blockchain proposes many innovative technologies to establish credible mechanisms in an open environment and therefore, it becomes a promising solution to the problem of credibility in educational development. To better understand the role of the blockchain, we aim to provide an extensive survey focusing on its key technology, application potential, and performance evaluation. First, from the perspective of blockchain characteristics, we summarize its application architecture in educational credibility. Next, we extensively discuss application potential of the blockchain, such as data storage, data sharing, achievement certification, and activity evaluation. Moreover, we investigate the performance evaluation, including basic performance metrics and specialized metrics for credibility. Finally, we analyze the challenges and research trends of blockchain in educational credibility and provide useful insights for future research.
      PubDate: Mon, 22 May 2023 01:50:00 +000
       
  • IOT and Blockchain-Based Cloud Model for Secure Data Transmission for
           Smart City

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      Abstract: The widespread use of the Internet of Things (IoT) technology has both good and bad things about it. There must be a full and reliable security system in place for the Internet of Things so that things can work together in a safe way and intrusions cannot happen. There are now many more ways to keep the Internet of Things safe, thanks to a detecting system. As machine learning and deep learning technologies have become better, a lot of good intrusion detection systems have been made. This kind of study is covered. These two types of security are compared in this study. The current machine-based intrusion detection system is broken down into more detailed categories based on detection technology, data source, architecture, and operating method. These categories are as follows: It is talked about how IoT security will grow in the future and how to understand its intrusion detection system too. In this paper, a cloud-based blockchain security model has been presented for secure data transmission over IoT.
      PubDate: Sat, 20 May 2023 05:50:00 +000
       
  • Contract-Based Incentive Mechanism for Redactable Proof-of-Stake
           Blockchains

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      Abstract: Blockchain has received a lot of attention due to its immutability. However, the immutability characteristic prohibits editing the blocks which need to be modified. Although the existing redactable blockchain enables to manipulate blocks in a controlled way, it may suffer from the security threats if the number of honest committee members (CMs) is insufficient. Thus, to attract honest CMs for validating and voting the editing blocks in permissionless blockchain, this paper presents a contract-based incentive mechanism between contract issuer and every CM. Firstly, it models the interaction between the contract issuer and each CM in the verifying and voting process. Secondly, it builds an incentive mechanism according to the contract issuer’s cost and the committee size. Finally, it selects a sufficiently large number of CMs with an optimization method. The analysis shows that the present mechanism is secure against Sybil attack, and the simulations demonstrate that the proposed mechanism is effective.
      PubDate: Wed, 17 May 2023 12:50:00 +000
       
  • Network Traffic Classification Based on SD Sampling and Hierarchical
           Ensemble Learning

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      Abstract: With the increase in cyber threats in recent years, there have been more forms of demand for network security protection measures. Network traffic classification technology is used to adapt to the dynamic threat environment. However, network traffic has a natural unbalanced class distribution problem, and the single model leads to the low accuracy and high false-positive rate of the traditional detection model. Given the above two problems, this paper proposes a new dataset balancing method named SD sampling based on the SMOTE algorithm. Different from the SMOTE algorithm, this method divides the sample into two types that are easy and difficult to classify and only balances the difficult-to-classify sample, which not only overcomes the SMOTE’s overgeneralization but also combines the idea of oversampling and undersampling. In addition, a two-layer structure combined with XGBoost and the random forest is proposed for multiclassification of anomalous traffic, since using a hierarchical structure can better classify minority abnormal traffic. This paper conducts experiments on the CICIDS2017 dataset. The results show that the classification accuracy of the proposed model is more than 99.70% and that the false-positive rate is less than 0.34%, indicating that the proposed model is better than traditional models.
      PubDate: Wed, 17 May 2023 02:05:00 +000
       
  • Self-Sovereign Identity for Consented and Content-Based Access to Medical
           Records Using Blockchain

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      Abstract: Electronic health records (EHRs) and medical data are classified as personal data in every privacy law, meaning that any related service that includes processing such data must come with full security, confidentiality, privacy, and accountability. Solutions for health data management, as in storing it, sharing and processing it, are emerging quickly and were significantly boosted by the COVID-19 pandemic that created a need to move things online. EHRs make a crucial part of digital identity data, and the same digital identity trends—as in self-sovereign identity powered by decentralized ledger technologies like blockchain, are being researched or implemented in contexts managing digital interactions between health facilities, patients, and health professionals. In this paper, we propose a blockchain-based solution enabling secure exchange of EHRs between different parties powered by a self-sovereign identity (SSI) wallet and decentralized identifiers. We also make use of a consortium IPFS network for off-chain storage and attribute-based encryption (ABE) to ensure data confidentiality and integrity. Through our solution, we grant users full control over their medical data and enable them to securely share it in total confidentiality over secure communication channels between user wallets using encryption. We also use DIDs for better user privacy and limit any possible correlations or identification by using pairwise DIDs. Overall, combining this set of technologies guarantees secure exchange of EHRs, secure storage, and management along with by-design features inherited from the technological stack.
      PubDate: Tue, 16 May 2023 10:35:00 +000
       
  • FACSC: Fine-Grained Access Control Based on Smart Contract for Terminals
           in Software-Defined Network

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      Abstract: Physical terminals provide network services to upper-layer applications, but their limited memory and processing power make it challenging to perform security updates and patches, leaving them vulnerable to known security threats. Attackers can exploit these weaknesses to control the terminals and attack the network. To restrict unauthorized access to the network and its resources, appropriate access control mechanisms are necessary. In this paper, we propose a fine-grained access control method based on smart contracts (FACSC) for terminals in software-defined networking (SDN). FACSC utilizes the attribute-based access control (ABAC) model to achieve fine-grained control over terminal access networks. To ensure the security and reliability of access control policies and terminal-related attribute information, we utilize smart contract technology to implement the ABAC model. Furthermore, we leverage the programming protocol-independent packet processor (P4) to filter and forward packets in the data plane based on the packet option field, enabling rapid terminal access. Experimental results show that our proposed method achieves fine-grained secure authentication of terminals in SDN networks with a low authentication processing overhead.
      PubDate: Mon, 15 May 2023 06:35:01 +000
       
  • Local Corner and Motion Key Point Trajectory Extraction for Facial Forgery
           Identification

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      Abstract: At present, the development of deep forgery technology has brought new challenges to media content forensics, and the use of deep forgery identification methods to identify forged audio and video has become a significant focus of research and difficulty. Deep forgery technology and forensic technology play a mutual game and promote each other’s development. This paper proposes a spatiotemporal local feature abstraction (STLFA) framework for facial forgery identification to solve the media industry challenges of deep forgery technology. To adequately utilize local facial features, we combine facial key points, key point movement, and facial corner points to detect forgery content. This paper establishes a spatiotemporal relation, which realizes face forgery detection by identifying abnormalities of facial keypoints and corner points for interframe judgments. Meanwhile, we utilize RNNs to predict the sequences from facial key point movement abnormalities and corner points for interframe. Experimental results show that our method achieves better performance than some existing methods and good anticompression forgery face detection performance on FF++.
      PubDate: Mon, 15 May 2023 03:35:00 +000
       
  • KTSDroid: A Framework for Android Malware Categorization Using the Kernel
           Task Structure

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      Abstract: The penetration of malicious applications in the Android market has enhanced the significance of designing malware mitigation systems for Android. Malware detection systems are being developed by examining applications using static and dynamic analysis techniques. The use of code obfuscation has highlighted the importance of dynamic analysis as many static analysis schemes can be evaded by code obfuscation strategies. In order to record the true working of the application, a volatile memory-based solution for application analysis is presented in this study. Time-based memory dumps are collected after interactions with an application. Process-specific artifacts of the application under analysis are extracted by examining the kernel task structure of memory. The features in the kernel task structure belong to nine broad categories based on their semantics. An important contribution of the study is the analysis of the kernel task structure for determining the set of effective categories and features for Android malware categorization. Three of the most important categories and fourteen valuable features are reported. The proposed system categorizes the applications into five classes: adware, banking Trojans, riskware, SMS Trojans, and benign. The proposed system is able to categorize applications with an average F1-score of 0.984, which is the highest score reported so far for multiclass Android malware categorization with a minimum number of kernel task structure-based features.
      PubDate: Sat, 13 May 2023 07:05:00 +000
       
  • Blockchain-Based Privacy-Preserving Sensor Data Sharing with Fine-Grained
           Authorization in Microgrid

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      Abstract: Microgrid is a power system that includes various energy sources (e.g., solar panels and wind turbines), where a number of device status and sensing data are collected and transmitted by smart sensors. Based on sensing-as-a-service in microgrid, sensor owners and sensor data consumers can effectively perform data sharing operations. However, the state-of-the-art sensor data sharing works in microgrid have the following two limitations: (i) cannot support fine-grained authorization for sensor owners and sensor data consumers and (ii) fail to simultaneously consider confidentiality and authenticity for sensor data sharing. To address the problems, in this article, we propose a lightweight privacy-preserving sensing data sharing system with fine-grained authorization in microgrid. Technically, we employed attribute-based signature methodology to design a fined-grained authorization mechanism for sensor data users. Moreover, a lightweight hyper elliptic curve-based signcryption scheme is employed to provide confidentiality and authenticity for sensor data sharing. To clarify the feasibility of our proposed system, we implement the system and evaluate the performance. The experimental results show that the system achieves small communication and time overhead, as well as highly acceptable gas consumption of smart contract.
      PubDate: Thu, 11 May 2023 15:05:01 +000
       
  • Defending Privacy Inference Attacks to Federated Learning for Intelligent
           IoT with Parameter Compression

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      Abstract: Federated learning has been popularly studied with people’s increasing awareness of privacy protection. It solves the problem of privacy leakage by its ability that allows many clients to train a collaborative model without uploading local data collected by Internet of Things (IoT) devices. However, there are still threats of privacy leakage in federated learning. The privacy inference attacks can reconstruct the privacy data of other clients based on GAN from the parameters in the process of iterations for global models. In this work, we are motivated to prevent GAN-based privacy inference attacks in federated learning. Inspired by the idea of gradient compression, we propose a defense method called Federated Learning Parameter Compression (FLPC) which can reduce the sharing of information for privacy protection. It prevents attackers from recovering the private information of victims while maintaining the accuracy of the global model. Extensive experimental results demonstrated that our method is effective in the prevention of GAN-based privacy inferring attacks. In addition, based on the experimental results, we propose a norm-based metric to assess the performance of privacy-preserving.
      PubDate: Thu, 11 May 2023 15:05:00 +000
       
  • Efficient Compression Sensing Mechanism Based WBAN System Using Blockchain

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      Abstract: The hybrid wireless sensor network is made up of Wireless Body Area Network (WBAN). Generally, many hospitals use cellular networks to support telemedicine. To provide the treatment to the patient on time, for this, an early diagnosis is required, for treatment. With the help of WBANs, collections and transmissions of essential biomedical data to monitor human health becomes easy. Compressor Sensing (CS) is an emerging signal compression/acquisition methodology that offers a protruding alternative to traditional signal acquisition. The proposed mechanism reduces message exchange overhead and enhances trust value estimation via response time and computational resources. It reduces cost and makes the system affordable to the patient. According to the results, the proposed scheme in terms of Compression Ratio (CR) is 18.18% to 88.11% better as compared to existing schemes. Also in terms of Percentage Root-Mean-Squared Difference (PRD) value, the proposed scheme is 18.18% to 34.21% better than with respect to existing schemes. The consensus for any new block is achieved in 24% less time than the Proof-of-Work (PoW) approach. The shallow CPU usage is required for the leader election mechanism. CPU utilization while the experiment lies in the range of 0.9% and 14%. While simulating a one-hour duration, the peak CPU utilization is 21%.
      PubDate: Thu, 11 May 2023 06:50:00 +000
       
  • DeepDefense: A Steganalysis-Based Backdoor Detecting and Mitigating
           Protocol in Deep Neural Networks for AI Security

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      Abstract: Backdoor attacks have been recognized as a major AI security threat in deep neural networks (DNNs) recently. The attackers inject backdoors into DNNs during the model training such as federated learning. The infected model behaves normally on the clean samples in AI applications while the backdoors are only activated by the predefined triggers and resulted in the specified results. Most of the existing defensing approaches assume that the trigger settings on different poisoned samples are visible and identical just like a white square in the corner of the image. Besides, the sample-specific triggers are always invisible and difficult to detect in DNNs, which also becomes a great challenge against the existing defensing protocols. In this paper, to address the above problems, we propose a backdoor detecting and mitigating protocol based on a wider separate-then-reunion network (WISERNet) equipped with a cryptographic deep steganalyzer for color images, which detects the backdoors hiding behind the poisoned samples even if the embedding algorithm is unknown and further feeds the poisoned samples into the infected model for backdoor unlearning and mitigation. The experimental results show that our work performs better in the backdoor defensing effect compared to state-of-the-art backdoor defensing methods such as fine-pruning and ABL against three typical backdoor attacks. Our protocol reduces the attack success rate close to 0% on the test data and slightly decreases the classification accuracy on the clean samples within 3%.
      PubDate: Tue, 09 May 2023 10:05:00 +000
       
  • Workload-Aware WiNoC Design with Intelligent Reconfigurable Wireless
           Interface

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      Abstract: By introducing wireless interfaces in conventional wired routers or hubs, wireless network-on-chip (WiNoC) is proposed to relieve congestion pressure from high volume inter-subnet data transmission. Generally, processing elements on chip receive input data and return feedback through network interface, and data transmission function in Network-on-Chip (NoC) is completed by routers. Hubs equipped with wireless interface are fixed to certain wired routers. While wireless channels may not be fully utilized due to unbalanced workload and constant hub-router connection, e.g., certain nodes processing excess inter-subnet data traffic are far away from hubs. In this paper, we proposed a workload-aware WiNoC design with intelligent reconfigurable wireless interface to improve wireless resources utilization and mitigate congestion. Through multidimensional analysis of traffic flow, a 4-layer neural network is trained offline and applied to analyze workload in each tile, and return three most potential tiles for wireless interface reconfiguration to fully utilize wireless channel and lowing latency. We also implement a historical traffic information-based reconfigurable scheme for comparation. Evaluation results show that in an 8 × 8 hybrid mesh topology, the proposed scheme can achieve 10%–16% reduction in network latency and 5%–11% increment in network throughput compared with fixed-link hub-node connection scheme under several mixed traffic patterns.
      PubDate: Tue, 09 May 2023 08:50:00 +000
       
  • CRT-Based Homomorphic Encryption over the Fraction

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      Abstract: Homomorphic encryption technology is the holy grail of cryptography and has a wide range of applications in practice. This paper proposes a homomorphic encryption scheme over the fraction based on the Chinese remainder theorem (CRT) Dayan qiuyi rule. This homomorphic scheme performs encryption and decryption operations by forming congruence groups and has homomorphism. The solution in this paper first combines the traditional CRT algorithm with the Dayan qiuyi rule to obtain the CRTF algorithm that can be operated on the fraction field. Finally, in the decryption process, modulo arithmetic is used twice to obtain the correct plaintext components, restored to plaintext by CRTF. The scheme’s security is related to a decisional version of an approximate GCD problem. The proof of theoretical derivation shows that this paper’s homomorphic encryption scheme can realize the homomorphic addition operation in the fraction field. Compared with the CKKS scheme, efficiency is improved.
      PubDate: Mon, 08 May 2023 08:35:00 +000
       
  • Twitter Bots in Cyber-Physical-Social Systems: Detection and Estimation
           Based on the SEIR Model

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      Abstract: Bots are now part of the social media landscape, and thus, a threat to cyber-physical-social systems (CPSSs). A better understanding of their characteristic behaviors and estimation of their impact on public opinion could help improve the algorithms to identify bots and help develop strategies to reduce their influence. The cosine function-based algorithm is able to compare the similarity between tweets and restore the course of information circulation. Combined with malicious features of an account, our method could effectively detect bots. We implement SEIR model to compute tweets with the hashtag #Huawei 5G and divide the trend propagation into the following four phases: formation, fermentation, explosion, and decay of trend. Sentiment analysis revealed the change of emotion and opinion among normal users in different stages and the manipulation attempt of bots behind it. Experiment results show that bots have very limited relation to users’ stance in whole. In early phase bots could affect those who are neutral. The influence of bots declines in later stage. Polarized views can hardly be changed.
      PubDate: Mon, 08 May 2023 07:50:00 +000
       
  • A Lightweight Cryptographic Algorithm Based on DNA Computing for IoT
           Devices

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      Abstract: Internet of Things (IoT) applications are used in almost every part of our life, so it is important to protect the sensitive data and information that is transmitted over wireless networks such as images and documents. The IoT devices have limited computational resources; they are called limited devices due to their limited processors and memory size. Traditional encryption methods require a lot of computing power; therefore, it is difficult to implement traditional cryptographic algorithm on IoT processor. Finally, a new, lightweight encryption method based on the DNA sequence is proposed to suit the IoT devices in a way to make an easy and secure the communications among the IoT devices. DNA sequences are very random, so we have used it to make a strong secret key that is hard for attackers to break. The proposed method has an advantage in terms of efficiency and strength. Experiments and security tests show that the proposed encryption system not only has a good encryption effect and can withstand known attacks, but it is also fast enough for real-world use. The DNA key is used to encrypt files using two simple and reliable methods such as substitution and transposition procedures that meet IoT computational requirements. In addition, when compared with other encryption algorithms, the experimental results shows that the key size, encryption time, and distortion preparation are all superior.
      PubDate: Mon, 08 May 2023 07:20:00 +000
       
  • DQfD-AIPT: An Intelligent Penetration Testing Framework Incorporating
           Expert Demonstration Data

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      Abstract: The application of reinforcement learning (RL) methods of artificial intelligence for penetration testing (PT) provides a solution to the current problems of high labour costs and high reliance on expert knowledge for manual PT. In order to improve the efficiency of RL algorithms for PT, existing research has considered bringing in the knowledge of PT experts and combining it with the use of imitative learning methods to guide the agent in its decision-making. However, the disadvantage of using imitation learning is also obvious; that is, the performance of the strategies learned by the agent hardly exceeds the demonstrated behaviour of the expert and it can also cause expert knowledge overfitting. At the same time, the expert knowledge in the currently proposed method is poorly interpretable and highly scenario-dependent. The expert knowledge used in these methods is not universal. To address these issues, we propose an intelligent PT framework named DQfD-AIPT. The framework encompasses the process of collecting and using expert knowledge and provides a rational definition of the structure of expert knowledge. To solve the overfitting problem, we perform PT path planning based on the deep Q-learning from demonstrations (DQfD) algorithm. DQfD combines the benefits of RL and imitation learning to effectively improve the PT strategy and performance of agents while avoiding overfitting. Finally, we conducted experiments in a simulated network scenario containing honeypots. The experimental results proved the effectiveness of expert knowledge incorporation. In addition, the DQfD algorithm can improve the efficiency of penetration testing more effectively than that by the classical deep reinforcement learning (DRL) method and can obtain a higher cumulative reward. Not only that, due to the incorporation of expert knowledge, in scenarios with honeypots, the DQfD method can effectively reduce the probability of interacting with honeypots compared to the classical DRL method.
      PubDate: Thu, 04 May 2023 04:20:00 +000
       
  • Uncovering Resilient Actions of Robotic Technology with Data
           Interpretation Trajectories Using Knowledge Representation Procedures

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      Abstract: This article highlights the importance of learning models which prevent the resilient attack of robotic technology with a subset of trajectories. Many complement models are introduced in the field of path planning robots without any knowledge of representation procedures, so robotic data are subject to different attacks from several users. During such attacks, the data will be misplaced and commands specified to robots will be disorganized so a new training data set has to be incorporated which is a difficult task. Therefore, to prevent probability of data failure time-dependent binary probability prototypes are introduced with low training data. Furthermore, a regularized boosting procedure (RBP) has been applied with different weights to switch multiple robots with discrete knowledge representation. Then a high space block is incorporated for maximizing coverage areas during loss functions and this is implicit as an innovative technique as compared with existing procedures. To validate the effectiveness of proposed learning techniques in robots, four scenarios are considered which include accuracy and success rate of detection. Subsequently, the outcomes prove that the robotic path with learning models are highly effective for an average percentile of 86% as compared to conventional techniques.
      PubDate: Wed, 03 May 2023 07:20:00 +000
       
  • Explore Gap between 3D DNN and Human Vision Utilizing Fooling Point Cloud
           Generated by MEHHO

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      Abstract: Deep neural network (DNN) has replaced humans to make decisions in many security-critical senses such as face recognition and automatic drive. Essentially, researchers try to teach DNN to simulate human behavior. However, many evidences show that there is a huge gap between humans and DNN, which has raised lots of security concern. Adversarial sample is a common way to show the gap between DNN and humans in recognizing objects with similar appearance. However, we argue that the difference is not limited to adversarial samples. Hence, this paper explores such differences in a new way by generating fooling samples in 3D point cloud domain. Specifically, the fooling point cloud is hardly recognized by human vision but is classified to the target class by the victim 3D point cloud DNN (3D DNN) with more than confidence. Furthermore, to search for the optimal fooling point cloud, a new evolutionary algorithm named Multielites Harris Hawk Optimization (MEHHO) with enhanced exploitation ability is designed. On one hand, our experiments demonstrate that: (1) 3D DNN tends to learn high-level features of one object; (2) 3D DNN that makes decisions relying on more points is more robust; and (3) the gap is hardly learned by 3D DNN. On the other hand, the comparison experiments show that the designed MEHHO outperforms the SOTA evolutionary algorithms w.r.t. statistics and convergence results.
      PubDate: Tue, 02 May 2023 09:05:01 +000
       
  • Reversible Data Hiding in Encrypted Image via Joint Encoding of Multiple
           MSB and Pixel Difference

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      Abstract: Reversible data hiding in encrypted image (RDHEI) has become a research hotspot, which can effectively protect image content privacy. An RDHEI scheme based on the joint encoding of multiple MSBs (most significant bits) and pixel difference is proposed in this paper. A block-based image encryption method is adopted on the content owner side, which can securely protect the image contents while retaining the spatial correlation within each block. By the joint encoding strategy of multiple MSB and pixel difference, the redundancy within the bit plane is sufficiently compressed to accommodate more additional data; thus, a high embedding rate can be achieved. According to different kinds of available keys, image decryption and data extraction can be separably conducted on the receiver side. Experimental results show that our scheme can achieve a higher embedding rate than some state-of-the-art schemes.
      PubDate: Sat, 29 Apr 2023 15:35:00 +000
       
  • Projective Synchronization Analysis of Master-Slave Complex Networks with
           Multiple Time-Varying Delays via Impulsive Control

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      Abstract: This article investigates the projective synchronization problem for a class of master-slave complex dynamical networks with multiple time-varying delays. A class of the delayed impulsive controller is designed; sufficient criterions for the projective synchronization of complex dynamical networks are derived. The nonlinear term and the coupled term have nonidentical time-varying delays, which increases our research difficulties. Two numerical simulations are presented to verify the effectiveness of our result.
      PubDate: Sat, 29 Apr 2023 05:35:00 +000
       
  • Privacy-Preserving Industrial Control System Anomaly Detection Platform

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      Abstract: With the development of IT technologies, an increasing number of industrial control systems (ICSs) can be accessed from the public Internet (with authentication). In such an open environment, cyberattacks become a serious threat to both ICS system integrity and data privacy. As a countermeasure, anomaly detection systems are often deployed to analyze the network traffic. However, due to privacy regulation, the network packages cannot be directly processed in plaintext in many countries. In this work, we present a privacy-preserving anomaly detection platform for ICS. The platform consists of three nodes running low-latency MPC protocols to evaluate the live network packages using decision trees on the fly with privacy assurance. Our benchmark result shows that the platform can process thousands of packages every ten seconds.
      PubDate: Fri, 28 Apr 2023 15:05:00 +000
       
  • A Robust Continuous Authentication System Using Smartphone Sensors and
           Wasserstein Generative Adversarial Networks

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      Abstract: Since the continuous authentication (CA) system based on smartphone sensors has been facing the challenge of the low-data regime under some practical scenarios, which leads to low accuracy of CA, it needs to be solved urgently. To this end, currently, the generative adversarial networks (GAN) provide a powerful method to train the result generative model that could generate very convincing verisimilar data. The framework of the GAN and its variants shed much light on improving the performance of CA. Therefore, in this article, we propose a continuous authentication system on smartphones based on a Wasserstein generative adversarial network (WGAN) for sensor data augmentation, which utilizes accelerometers, gyroscopes, and magnetometers of smartphone sensors to sense phone movements caused by user operation behavior. Specifically, based on sensor data under different user activities, the WGAN is used to create additional data in training data for data augmentation. With the augmented data, we design a convolutional neural network to learn and extract deep features from sensor data, and then use four classifiers of RF, OCSVM, DT, and KNN to train these features. Finally, we train and test on the HMOG dataset, and the results show that the EER of the authentication system is between 3.68% and 6.39% on the sensor data with a time window of 2 s.
      PubDate: Wed, 26 Apr 2023 05:20:01 +000
       
  • Embedded Gateway Security Detection Technology Based on the Deep Neural
           Network Rule Extraction

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      Abstract: Aiming at the network security problem of power system cable trench control industrial Internet system, we studied an intrusion detection method applied to the embedded industrial Internet of Things gateway. This method extracts rules from the DBN-DNN deep neural network to obtain intrusion detection models that are conducive to integration into embedded systems. We first use the DBN network to reduce the dimensionality of the data, then use the DNN to train the classification model, and extract the rules from the DNN’s neurons to form a rule tree for intrusion detection. The KDD CUP99 training database is used to verify the feasibility of the method, and the test is carried out in the embedded gateway. The results show that the detection method based on rule extraction used in this paper can ensure detection efficiency and accuracy compared to the traditional detection methods. At the same time, it saves more computing resources and is more conducive to integration in embedded gateway systems.
      PubDate: Tue, 25 Apr 2023 05:20:00 +000
       
  • Research on Network Behavior Risk Measurement Method Based on Traffic
           Analysis

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      Abstract: At present, the network security problem is facing a serious threat, and network security events continue to occur. It has become an important link to prevent network attacks and ensure network security. According to the network security protection measures and security technical requirements, it has become an urgent need to establish appropriate security measurement methods and strengthen the monitoring and analysis of network security status. This study proposes a network behavior risk measurement method based on traffic analysis to accurately and objectively evaluate the security state of the network. Traffic is the most basic behavior of the network and the basis of security risk measurement. Firstly, we regard the traffic data as network behavior to build scenarios. Through differential manifold modeling, the traffic data and topology of the network system are semantically described to form a matrix. Then, after manifold dimensionality reduction, the objective risk assessment value can be obtained by manifold mapping and Riemann metric. In this study, the differential manifold theory is applied to network behavior risk measurement, and the innovation of differential manifold in the field of network behavior risk measurement is given. After giving the network behavior risk measurement theory, we first verify the effectiveness of the proposed method through the simulation experiments. Secondly, the public CIC-IDS-2017 data set is used for analysis and calculation to prove the accuracy of the proposed method.
      PubDate: Mon, 24 Apr 2023 15:35:00 +000
       
 
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