Transfer learning for securing electric vehicle charging infrastructure from cyber-physical attacks by Ahmad Almadhor, Shtwai Alsubai, Imen Bouazzi, Vincent Karovic, Monika Davidekova, Abdullah Al Hejaili, Gabriel Avelino Sampedro

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Design of an iterative method for disease prediction in finger millet leaves using graph networks, dyna networks, autoencoders, and recurrent neural networks by Shailendra Tiwari, Anita Gehlot, Rajesh Singh, Bhekisipho Twala, Neeraj Priyadarshi

“…This work proposes a comprehensive framework for detection and prediction of the disease in Finger Millet leaves using a combination of Graph Networks, Dyna Networks, Autoencoders, and Recurrent Neural Networks. …”
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A secure and efficient deep learning-based intrusion detection framework for the internet of vehicles by Hasim Khan, Ghanshyam G. Tejani, Rayed AlGhamdi, Sultan Alasmari, Naveen Kumar Sharma, Sunil Kumar Sharma

“…Vision Transformer (ViT), wavelet transforms, and GAT ensure effective feature extraction, and a novel hybrid optimization known as Crayfish-Mother secure Optimization (CMSO) method is proposed to optimize feature selection to its maximum and reduce computational cost. …”
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An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security by J. Maruthupandi, S. Sivakumar, B. Lakshmi Dhevi, S. Prasanna, R. Karpaga Priya, Shitharth Selvarajan

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Securing IoT-Cloud Applications with AQ-KGMO-DMG Enhanced SVM for Intrusion Detection by Konduru Siva Naga Narasimharao, P. V. Lakshmi

“…Intrusion detection, as a cornerstone of information security, plays a pivotal role in fortifying networks against potential threats, emphasizing the necessity for robust and reliable methods capable of discerning and mitigating network vulnerabilities effectively. …”
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Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems by Engy El-Shafeiy, Walaa M. Elsayed, Haitham Elwahsh, Maazen Alsabaan, Mohamed I. Ibrahem, Gamal Farouk Elhady

“…Convolutional neural networks (CNN) are used for spatial feature extraction and superfluous data are filtered to improve computing efficiency. …”
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Improvement of the Regulatory Framework of Information Security for Terminal Access Devices of the State Information System by V. A. Sizov, D. M. Malinichev, V. V. Mochalov

“…Such diversity and heterogeneity of state information systems, on the one hand, and the need for high-quality state regulation in the field of information security in these systems, on the other hand, require the study and development of legal acts that take into account primarily the features of systems that have a typical modern architecture of “thin customer". …”
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An explainable federated blockchain framework with privacy-preserving AI optimization for securing healthcare data by Tanisha Bhardwaj, K. Sumangali

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Security-Enhanced Lightweight Authentication Key-Agreement Protocol for Unmanned Aerial Vehicle Communication by Zhoucan He, Yilong Zheng, Sisi Chen, Zhongze Du, Shuyuan Liu, Kailong Zhang

“…Unmanned aerial vehicles have been widely employed in recent years owing to their remarkable features such as low environmental requirements and high survivability, and a new tendency towards networking, intelligence, and collaboration has emerged. …”
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Lightweight terminal cross-domain authentication protocol in edge computing environment by Hongying ZHU, Xinyou ZHANG, Huanlai XING, Li FENG

“…Edge computing has gained widespread usage in intelligent applications due to its benefits, including low latency, high bandwidth, and cost-effectiveness.However, it also faces many security challenges due to its distributed, real-time, multi-source and heterogeneous data characteristics.Identity authentication serves as the initial step for terminal to access to the network and acts as the first line of defense for edge computing.To address the security issues in the edge computing environment, a terminal cross-domain authentication protocol suitable for the edge computing environment was proposed based on the ＆quot;cloud-edge-end＆quot; three-level network authentication architecture.Access authentication was implemented between terminals and local edge nodes based on the SM9 algorithm, and session keys were negotiated.The secret key was combined with symmetric encryption technology and hash function to achieve cross-domain authentication for the terminal.The pseudonym mechanism was used in the authentication process to protect the privacy of end users.The terminal only needs to register once, and it can roam randomly between different security domains.BAN logic was used to prove the correctness of the protocol and analyze its security.The results show that this protocol is capable of resisting common attacks in IoT scenarios, and it features characteristics such as single sign-on and user anonymity.The performance of the cross-domain authentication protocol was evaluated based on computational and communication costs, and compared with existing schemes.The experimental results show that this protocol outperforms other schemes in terms of computational and communication costs, making it suitable for resource-constrained terminal devices.Overall, the proposed protocol offers lightweight and secure identity authentication within edge computing environments.…”
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Edge computing privacy protection method based on blockchain and federated learning by Chen FANG, Yuanbo GUO, Yifeng WANG, Yongjin HU, Jiali MA, Han ZHANG, Yangyang HU

“…Aiming at the needs of edge computing for data privacy, the correctness of calculation results and the auditability of data processing, a privacy protection method for edge computing based on blockchain and federated learning was proposed, which can realize collaborative training with multiple devices at the edge of the network without a trusted environment and special hardware facilities.The blockchain was used to endow the edge computing with features such as tamper-proof and resistance to single-point-of-failure attacks, and the gradient verification and incentive mechanism were incorporated into the consensus protocol to encourage more local devices to honestly contribute computing power and data to the federated learning.For the potential privacy leakage problems caused by sharing model parameters, an adaptive differential privacy mechanism was designed to protect parameter privacy while reducing the impact of noise on the model accuracy, and moments accountant was used to accurately track the privacy loss during the training process.Experimental results show that the proposed method can resist 30% of poisoning attacks, and can achieve privacy protection with high model accuracy, and is suitable for edge computing scenarios that require high level of security and accuracy.…”
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Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments by Hanan Abbas Mohammad

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DynBlock: dynamic data encryption with Toffoli gate for IoT by Mubasher Haq, Ijaz Ali Shoukat, Alamgir Naushad, Mohsin Raza Jafri, Moid Sandhu, Abd Ullah Khan, Hyundong Shin

“…We evaluate DynBlock with both 3 and 5 rounds to assess its security and computational efficiency. While the 5-round configuration offers stronger resistance to cryptanalytic attacks, the 3-round setup already delivers robust security, featuring high entropy and a strong avalanche effect, making it well-suited for resource-constrained environments. …”
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Overview of detection techniques for malicious social bots by Rong LIU, Bo CHEN, Ling YU, Ya-shang LIU, Si-yuan CHEN

“…The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.…”
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Overview of detection techniques for malicious social bots by Rong LIU, Bo CHEN, Ling YU, Ya-shang LIU, Si-yuan CHEN

“…The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.…”
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A Blockchain Parallel Activity Architecture with Social Network Graphs as Carriers for Internet of Things Networks by Xin Cong, Lingling Zi

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A Cloud User Anomaly Detection Method Based on Mouse Behavior by XU Hong-jun, ZHANG Hong, HE Wei

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Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security by Harshala Shingne, Diptee Chikmurge, Priya Parkhi, Poorva Agrawal

“…Finally, it considers the optimization of cryptographic protocols in a distributed quantum network using Multi-Agent Deep Q-Networks. This multi-agent system strengthens both the security and computational efficiency by reducing attack vulnerabilities by 15–18 % and lowering the computational complexity by 20–25 %. …”
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A lightweight speaker verification approach for autonomous vehicles by Yousef Salah, Omar Shalash, Esraa Khatab

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A High-Performance and Lightweight Maritime Target Detection Algorithm by Shidan Sun, Zhiping Xu, Xiaochun Cao, Jiachun Zheng, Jiawen Yang, Ni Jin

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