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7th International Conference on computer Information science and Application Technology, CISAT 2024

作者： Zhou, Jinzhong Jiang, Rui Chen, Gangyong Wang, Lile Southeast University School of Cyber Science and Engineering Nanjing210096 China Natl. Eng. Research Center of Classified Protection and Safeguard Technology for Cybersecurity Shanghai200031 China

ISBN: (纸本)9798350375107

With the development of machine learning, artificial intelligence (AI) technology has reshaped our life in many aspects. Federated learning is a typical machine learning training architecture, which aims to train the model without sharing the sensitive privacy data. However, many researches show that there is still a privacy leakage possibility for federated learning. Network attackers can eavesdrop on the communication between clients and server and infer the privacy information from transmitted parameters. In addition, an honest-but-curious server which orchestrates the whole training process is able to access the privacy data. In order to deal with these problems, in this paper, we propose a homomorphic encryption based full flow privacy protection (FFPP) scheme for federated learning. Firstly, our FFPP scheme can protect the parameters transmitted between the clients and server from being leaked. We apply the additional homomorphic encryption algorithm to protect the data in federated learning. Secondly, with the additional homomorphic property, our FFPP scheme enables the honest-but-curious server to aggregate the global model parameters for all clients. We design a key management center to assist the server to aggregate the global parameters without accessing them directly. Finally, we formally prove the correctness and security of our FFPP scheme and do the experiment. The experiment performance shows that our FFPP scheme is more efficient than other related works. © 2024 IEEE.

关键词： Differential privacy

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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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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Towards Energy-efficient Resource Allocation for Federated Learning in Mobile Edge Computing

Towards Energy-efficient Resource Allocation for Federated L...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Ma, Wenqiang Zhao, Yong Sun, Wen Liu, Yuan Guo, Bin Niyato, Dusit Northwestern Polytechnical University School of Cybersecurity Xi'an China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou China Northwestern Polytechnical University School of Computer Science Xi'an China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore

ISBN: (纸本)9798350312270

Federated learning has become a promising technology that enables edge devices to participate intelligent modeling without sharing data, and thus realizing edge intelligence in mobile edge computing (MEC). In this paper, we propose an energy-efficient resource allocation framework for federated learning in MEC. Different from existing works, we consider the heterogeneous and the dynamic nature (e.g., stragglers, diverging interests, and intermittent drop-out) of edge devices and their effects on the convergence and energy efficiency of federated learning. The proposed framework leverages multi-agent reinforcement learning to enable different devices to flexibly modify their federated learning policies based on the environment and their own status. The convergence and energy efficiency of federated learning can be further improved through collaborative decision-making and mutual compromise among devices. The numerical results showed that the proposed framework could greatly improve the convergence performance of federated learning model compared to baselines while achieving efficient and sustainable use of energy. © 2023 IEEE.

关键词： Reinforcement learning

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Detecting cross-site scripting attacks using machine learning: A systematic review

Detecting cross-site scripting attacks using machine learnin...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Baniya, D. Amrita Chaudhary, A. Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

In today s age, many daily tasks are performed through the Internet using various web applications. While using the web application, the information and data are stored in the database of the network which can easily be attacked by the attacker. The attacker used different types of attack patterns to steal the victim s information or any details like their bank information, and business details. Among the different types of attacks over the web application vulnerabilities, the most prominent category of attack is a cross-site scripting (XSS) attack. In the XSS attack, an attacker transmits the script to the victim s web Browser. As the victims are unaware that they were being attacked leading to stealing the valuable information like personal details, bank details, and cookies. Using the cookies of the victim, the attacker can easily access their account without using a username and password. Early, different types of approaches have been done to shoot out the vulnerabilities related to XSS. This paper purports to be a survey on XSS. The primary contribution of this study to the current state-of-the-art XSS is a systematic review of various types of research done to detect XSS vulnerabilities using Machine Learning. This paper also provides the background related to XSS, techniques, difficulties and future guidelines in the field of XSS to provide researchers with significant knowledge for further work. © 2024 The Author(s).

关键词： Machine learning

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SLARD: A Chinese Superior Legal Article Retrieval Dataset 31

SLARD: A Chinese Superior Legal Article Retrieval Dataset

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31st International Conference on Computational Linguistics, COLING 2025

作者： Chen, Zhe Ren, Pengjie Sun, Fuhui Wang, Xiaoyan Li, Yunjun Zhao, Siwen Yang, Tengyi School of Information Science and Engineering Shandong University China School of Computer Science and Technology Shandong University China Information Technology Service Center of People's Court

ISBN: (纸本)9798891761964

Retrieving superior legal articles involves identifying relevant legal articles that hold higher legal effectiveness. This process is crucial in legislative work because superior legal articles form the legal basis for drafting new laws. However, most existing legal information retrieval research focuses on retrieving legal documents, with limited research on retrieving superior legal articles. This gap restricts the digitization of legislative work. To advance research in this area, we propose SLARD: A Chinese Superior Legal Article Retrieval Dataset, which filters 2,627 queries and 9,184 candidates from over 4.3 million effective Chinese regulations, covering 32 categories, such as environment, agriculture, and water resources. Each query is manually annotated, and the candidates include superior articles at both the provincial and national levels. We conducted detailed experiments and analyses on the dataset and found that existing retrieval methods struggle to achieve ideal results. The best method achieved a R@1 of only 0.4719. Additionally, we found that existing large language models (LLMs) lack prior knowledge of the content of superior legal articles. This indicates the necessity for further exploration and research in this field. © 2025 Association for Computational Linguistics.

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Auto Insurance Fraud Detection with Multimodal Learning

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Data Intelligence 2023年第2期5卷 388-412页

作者： Jiaxi Yang Kui Chen Kai Ding Chongning Na Meng Wang Financial Technological Research Center Zhejiang LabHangzhou 361005China School of Computer Science and Engineering Southeast UniversityNanjing 211189China

In recent years,feature engineering-based machine learning models have made significant progress in auto insurance fraud ***,most models or systems focused only on structural data and did not utilize multi-modal data to improve fraud detection *** solve this problem,we adapt both natural language processing and computer vision techniques to our knowledge-based algorithm and construct an Auto Insurance Multi-modal Learning(AIML)*** then apply AIML to detect fraud behavior in auto insurance cases with data from real scenarios and conduct experiments to examine the improvement in model performance with multi-modal data compared to baseline model with structural data only.A selfdesigned Semi-Auto Feature Engineer(SAFE)algorithm to process auto insurance data and a visual data processing framework are embedded within *** show that AIML substantially improves the model performance in detecting fraud behavior compared to models that only use structural data.

关键词： Auto Insurance Multi-modal Learning Fraud detection Ensemble learning

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Review of permission-based malware detection in Android

Review of permission-based malware detection in Android

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Rawat, Nishant Amrita Singh, Avjeet Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

A market study showed that an average of 70% of smartphone users use an android-based smartphone. The Android operating system draws numerous malware threats as a result of its popularity. The statistic reveals that 97% of malware prey on android due to its lack of security. The malware enters the system and after installing the malware application they ask permission from the user to run. Permissions like Read_Phone, Access_Internet, Write_Log, etc give full control of the victim’s phone to the hacker. In recent years more efforts were made to analyze android permissions to detect malware. In this paper, we review some of the already published work in detail which is in the area of malware detection using static analysis. © 2024 The Author(s).

关键词： Machine learning

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Metaheuristic Optimization of Trajectory and Dynamic Time Splitting for UAV Communication Systems

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IEEE Communications Letters 2025年第6期29卷 1200-1204页

作者： Chien, Trinh Van Quan, Nguyen Minh Shin, Oh-Soon Nguyen, Van-Dinh Hanoi University of Science and Technology School of Information and Communications Technology Hanoi100000 Viet Nam Soongsil University School of Electronic Engineering Seoul06978 Korea Republic of Center for Environmental Intelligence VinUniversity College of Engineering and Computer Science Vinhomes Ocean Park Hanoi100000 Viet Nam

The integration of unmanned aerial vehicles (UAVs) into wireless communication systems has emerged as a transformative approach, promising cost-efficient connectivity. This paper addresses the optimization of the dynamic time-splitting ratio and flight trajectory for a communication system linking a ground base station to the UAV equipped with backscatter devices (referred to as UB), and from UB to an end user. Given the inherent non-convexity of the problem, we develop two meta-heuristic-based approaches inspired by genetic algorithm and particle swarm optimization to enhance the total achievable rate while reducing computational complexity. Numerical results demonstrate the effectiveness of these meta-heuristic solutions, showcasing significant improvements in the achievable rate and computation time compared to existing benchmarks. © 1997-2012 IEEE.

关键词： Genetic algorithms

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CAKG: A Framework for cybersecurity Threat Detection of Automotive via Knowledge Graph 8

CAKG: A Framework for Cybersecurity Threat Detection of Auto...

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8th International Conference on Data science in Cyberspace, DSC 2023

作者： Yang, Peng Lijie, Wang Yun, Li Xuedong, Song Yaxin, Wang Biheng, Guo School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Cybersecurity Research Institute Guangdong Weichen Information Technology Co. Ltd Dongguan China

ISBN: (纸本)9798350331035

With the increasing complexity and connectivity of vehicles, ensuring their security has become a critical concern. In this study, we propose CAKG: A Framework For cybersecurity Threat Detection Of Automotive Via Knowledge Graph, achieved with a knowledge graph for vehicle vulnerability and threat intelligence. We integrate existing cyber security knowledge bases to analyze potential attack surfaces and scenarios specific to automobiles. By using keyword extraction and text similarity analysis, we identify threats relevant to the event produced by Intrusion Detection Systems(IDS) in automotive. Moreover, we leverage another knowledge graph to analyze attack logs gained from actual vehicles, enabling us to further correlate security events and product situational analysis. Our framework provides a holistic perspective on vehicle security, facilitating threat modeling and enhancing our understanding of potential attack scenarios. © 2023 IEEE.

关键词： Attack Graph cybersecurity Knowledge Graph Intelligent Connected Vehicles Threat Intelligence Vulnerability Analysis

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