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Artificial intelligence in physiological characteristics recognition for internet of things authentication

Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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security Flaw in TCP/IP and Proposed Measures 1

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International Conference on Recent Developments in cyber security, ReDCySec 2023

作者： Upadhyay, Sourav Kumar Kumar, Prakash Department of Computer Science and Engineering BIT Sindri Dhanbad India Department of Computer Science and Cyber Security JRSU Ranchi India

ISBN: (数字)9789819998111

ISBN: (纸本)9789819998104

The whole Internet is based on TCP/IP protocol suite. The inherent flaw in the TCP/IP makes a cascading effect for other attacks like flooding, fingerprinting, and many more. All the switching and routing mechanisms are based on the TCP/IP, so it is safe to say that TCP/IP is the backbone protocol suite for communication for all major Internet applications. The architectural flaw of TCP/IP protocol and possible ways to mitigate those flaws are discussed in this paper. Especially, the problem with connection phase in TCP and how SYN/ACK/RST packets can be used to breach an ongoing handshake are brought in light. In order to help create a safe information network environment in the future, this study aims to provide some references. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Transmission control protocol

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IDCE:Integrated Data Compression and Encryption for Enhanced security and Efficiency

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computer Modeling in engineering & Sciences 2025年第4期143卷 1029-1048页

作者： Muhammad Usama Arshad Aziz Suliman A.Alsuhibany Imtiaz Hassan Farrukh Yuldashev Department of Cyber Security Pakistan Navy Engineering CollegeNational University of Sciences and TechnologyKarachi75350Pakistan Department of Computer Science Main CampusIqra UniversityKarachi75500Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100170Uzbekistan

Data compression plays a vital role in datamanagement and information theory by reducing ***,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and *** the exponential growth of digital data,robust security measures are *** encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key *** their individual benefits,both require significant computational ***,performing them separately for the same data increases complexity and processing *** the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and *** on 128-bit block sizes and a 256-bit secret key *** combines Huffman coding for compression and a Tent map for ***,an iterative Arnold cat map further enhances cryptographic confusion *** analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.

关键词： Chaotic maps security data compression data encryption integrated compression and encryption

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Dynamic Graph-Enhanced Sequential LSTM for Context-Aware Point-of-Interest Recommendation 21

Dynamic Graph-Enhanced Sequential LSTM for Context-Aware Poi...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Yuwen, Zhang Shaobo, Ren School of Computer Science and Engineering School of Cyber Security University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

Next Point-of-Interest (POI) recommendation is a critical task in location-based services, aiming to predict the user's next POI based on historical check-in data. Existing approaches often rely on static graphs or sequential models to capture user preferences, which are limited by the sparsity of check-in data and the inability to dynamically model spatio-temporal transitions. To address these challenges, we propose the Dynamic Graph-Enhanced LSTM (DGEL) model for next POI recommendation. DGEL constructs dynamic POI transition graphs by combining Transformer-based graph learning with embedding techniques, capturing implicit relationships between POIs. Additionally, a Customized Sequential LSTM (CS-LSTM) is introduced to model long-term and short-term dependencies, incorporating spatial, temporal, and relational signals. Experimental results on Gowalla and Foursquare datasets demonstrate that DGEL achieves significant performance improvements over state-of-the-art baselines, highlighting its effectiveness in leveraging dynamic graphs and structured sequential learning for POI recommendation. © 2024 IEEE.

关键词： Knowledge graph

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Multi-Behavioral Recommender System with Heterogeneous Graph Learning 4

Multi-Behavioral Recommender System with Heterogeneous Graph...

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4th International Conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Feng, Jiaming Liu, Yang Zhang, Junjie School of Computer Science and Engineering School of Cyber Security University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350384062

With the rapid development of online platforms and the explosive growth of user-generated data, personalized recommendation systems have become increasingly crucial in delivering relevant content to users. Traditional recommendation approaches often focus on modeling user-item interactions, neglecting the rich contextual information associated with user behaviors. In this paper, we propose a novel multi-behavior recommendation system based on heterogeneous graph learning. By leveraging the power of heterogeneity, our model integrates various types of user behaviors, such as browsing, adding cart, and purchasing, into a unified framework. We design a graph-based representation for capturing the complex relationships between users, items, and behaviors. Additionally, we introduce a graph neural network approach to effectively propagate and aggregate information across the heterogeneous graph. Experimental results on a real-world dataset demonstrate that our proposed system outperforms state-of-the-art recommendation methods in terms of accuracy and diversity. The findings highlight the potential of heterogeneous graph learning in enhancing the performance of multi-behavior recommendation systems. © 2024 IEEE.

关键词： Recommender systems

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Enhancing the security of SDN in 5G: A Hybrid Feature Selection Based Ensemble Machine Learning Framework for Classification of cyber-Attacks

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SN computer Science 2025年第3期6卷 1-16页

作者： Singh, Mahendra Pratap Haimashreelakshmi Singh, Virendra Pratap Gupta, Maanak Cyber Security Research Lab (CSRL) Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangaluru 575025 India Department of Computer Science Tennessee Technological University Cookeville TN United States

In recent years, the adoption of 5G has significantly increased due to its numerous benefits, including high availability, lower latency, improved reliability, and high performance. To manage packet flow, 5G relies on Software-Defined Networking (SDN) that employs software controllers and Application Programming Interfaces (APIs) to route packets and communicate with the hardware, providing advantages like high efficiency, low cost, and dependability. However, due to centralized control, SDN controllers are vulnerable to various cyber-attacks, including Distributed Denial of Service (DDoS), Denial of Service (DoS), Password Brute Forcing, Web Attacks, etc. This paper proposes a framework that comprises a hybrid feature selection method and an ensemble machine learning model. The proposed ensemble model combines the strengths of three different machine learning (ML) classifiers to create a voting classifier for classifying traffic in SDN. Additionally, the optimal value for the hyperparameters of each classifier is obtained through hyperparameter tuning. Finally, the experimental analysis of the proposed model using the InSDN dataset shows 99.96% accuracy, highlighting the proposed model’s effectiveness in addressing the limitations of the existing approaches and detecting multiple attacks in the SDN context. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G Hybrid feature selection Machine learning Software defined networking

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Distortion Evaluation of EMP Sensors Using Associated-Hermite Functions

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computers, Materials & Continua 2023年第1期74卷 1093-1105页

作者： Rupo Ma Siping Gao College of Command and Control Engineering Army Engineering UniversityNanjing210007China Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Department of Electrical and Computer Engineering National University of Singapore119260Singapore

Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and *** is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole ***,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement *** fidelity is usually employed to evaluate the distortion of an ***,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP *** this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual *** system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite *** of a sensor *** is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the *** of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.

关键词： Electromagnetic pulse associated-hermite functions system transfer matrix distortion rate

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CSA4Rec: Collaborative Signals Augmentation Model Based on GCN for Recommendation 25th

CSA4Rec: Collaborative Signals Augmentation Model Based on ...

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Liu, Haibo Yu, Lianjie Si, Yali Liu, Jinglian School of Cyber Security and Computer Hebei University Baoding Baoding China School of Computer Science and Engineering Changshu Institute of Technology Suzhou China

ISBN: (纸本)9789819605699

Graph Convolution Network (GCN) is a potent deep learning methodology and GCN-based graph data augmentation methods excel in recommender systems. The augmented data, as the higher-order collaborative signals, is generated by pre-training the GCN model on raw data, before making the final prediction. Despite their success, three problems have always existed. First, during the pre-training phase, over-smooth and data sparsity have a serious impact on the quality of augmented graph data. Second, the influence degree of higher-order collaborative signals are difficult to control, resulting in unstable embeddings. Last, the output of the pre-trained model is only used to augment the data and is not utilized in subsequent training phases, leading to performance penalty. In this study, we propose an efficient Collaborative Signals Augmentation model based on GCN for Recommendation (CSA4Rec). In the pre-training phase, graph contrastive learning is utilized to generate distinct embeddings to address over-smooth and data sparsity. To manipulate the higher-order collaborative signals effectively, the signals are generated from raw data in the modeling phase and aggregated independently by GCN in the training phase. The Residual-like connection is employed to leverage the output of the pre-trained model to improve the convergence speed in the next training phase. Extensive experiments demonstrate the effectiveness and rationality of CSA4Rec. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques 18

Advancing Android Malware Detection with BioSentinel Neural ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cybersecurity Deep Learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day Threats

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Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection 18

Adapting to Evasive Tactics through Resilient Adversarial Ma...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Krishna, G. Bala Sravan Kumar, G. Ramachandra, Mummadi Sampurnima Pattem, K. Rani, D. Sandhya Kakarla, Geeta Cvr College of Engineering Department of Computer Science Engg. Rangareddy Ibrahimpatnam India Cvr College of Engineering Department of Computer Science Engg.-Cyber Security Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Hyderabad India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial Learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. These results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. The study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Block chain Blockchains Challenges of Block chain Consensus algorithm Data privacy Digital signatures Distributed ledger Faces Organizations Smart contracts

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