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Deep Learning Based Side-Channel Attack Detection for Mobile Devices security in 5G Networks

清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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TRE-DSP:A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

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Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Yousheng Zhou Rundong Peng Yuanni Liu Pandi Vijayakumar Brij Gupta College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Computer Science and Engineering University College of EngineeringTindivanamMelpakkamTamil nadu 604001India Department of Computer Engineering National Institute of Technology KurukshetraIndia

With the popularity of the Internet of Vehicles(IoV),a large amount of data is being generated every *** to securely share data between the IoV operator and various value-added service providers becomes one of the critical *** to its flexible and efficient fine-grained access control feature,Ciphertext-Policy Attribute-Based Encryption(CP-ABE)is suitable for data sharing in ***,there are many flaws in most existing CP-ABE schemes,such as attribute privacy leakage and key *** paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP).A partially hidden access structure is adopted to hide sensitive user attribute values,and attribute categories are sent along with the ciphertext to effectively avoid privacy *** addition,key tracking and malicious user revocation are introduced with broadcast encryption to prevent key *** the main computation task is outsourced to the cloud,the burden of the user side is relatively *** of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.

关键词： Privacy-preserving Attribute-based encryption Partially hidden policy Traceability User revocation Internet of vehicles

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3GPP Edge–Fog federation: Transparent 3rd-party authentication and application mobility

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computer Communications 2024年 222卷 220-229页

作者： Ali, Asad Islam, Minhajul Mallick, Tushin Islam, Mohammad Sakibul Sakib, Sadman Shohrab Hossain, Md. Lin, Ying-Dar National Institute of Cyber Security Ministry of Digital Affairs Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh

3GPP edge and fog computing paradigms provide computational services to users at low latency. These paradigms alone are not enough to fulfill the users’ requirements completely. Therefore, a federation among these computing paradigms is necessary. To realize such federation, there is a need of an authentication mechanism where subscribers of a fog, can access 3GPP edge's services, or vice versa, without buying new subscription, and an application mobility mechanism for continuous service during handover from 3GPP edge to fog, or vice versa, without re-authentication. In this work, we propose: (1) a proxy-based state transfer and third-party authentication (PS3A), that uses a transparent proxy to transfer the authentication and application state information between 3GPP edge and fog, and (2) a token-based state transfer and proxy-based third-party authentication (TSP3A), that uses the proxy to transfer the authentication information and tokens to transfer application state information between 3GPP edge and fog. The proxy plays different roles, via virtual counterparts of entities involved in these protocols, to provide transparency. When the 3GPP edge, using EPS-AKA, receives an authentication request, the proxy relays and behaves as a virtual Home Subscriber Server (vHSS) for the 3GPP edge, and behaves as a virtual user for the fog, which is using OIDC. We applied PS3A and TSP3A to three federation scenarios among 3GPP edge and fog. Experimental results show that PS3A and TSP3A provide authentication within 0.345–2.858 s for a 0–100 Mbps proxy load. The results further show that TSP3A provides application mobility, while taking 40%–52% less time than PS3A, using state tokens. TSP3A and PS3A also reduce the service interruption latency by 82.4% and 84.6%, compared to the cloud-based service, via tokens and prefetching. © 2024 Elsevier B.V.

关键词： Authentication

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A hybrid encryption for secure data deduplication the cloud

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International Journal of Cloud Computing 2023年第2-4期12卷 295-307页

作者： Ebinazer, Silambarasan Elkana Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Science and Engineering Cyber Security IIIT Kottayam India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redundant data in the cloud by leaving a single copy. This paper provides a hybrid encryption scheme using genetic algorithm (GA) and attribute-based encryption (ABE) to ensure secure data transmission in the cloud and preventing the data deduplication by using message locked encryption (MLE). The hybrid encryption scheme provides a secure data transmission in the cloud. From the simulation output, the time requirement is reduced up to 94% and consumes 43% bandwidth compared with existing works. The proposed method tends to be quite effective in reducing storage and bandwidth costs. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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MODELGUARD: Information-Theoretic Defense Against Model Extraction Attacks 33

MODELGUARD: Information-Theoretic Defense Against Model Extr...

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33rd USENIX security Symposium, USENIX security 2024

作者： Tang, Minxue Dai, Anna DiValentin, Louis Ding, Aolin Hass, Amin Gong, Neil Zhenqiang Chen, Yiran Li, Hai Department of Electrical and Computer Engineering Duke University United States Cyber Security Lab Accenture

ISBN: (纸本)9781939133441

Malicious utilization of a query interface can compromise the confidentiality of ML-as-a-Service (MLaaS) systems via model extraction attacks. Previous studies have proposed to perturb the predictions of the MLaaS system as a defense against model extraction attacks. However, existing prediction perturbation methods suffer from a poor privacy-utility balance and cannot effectively defend against the latest adaptive model extraction attacks. In this paper, we propose a novel prediction perturbation defense named MODELGUARD, which aims at defending against adaptive model extraction attacks while maintaining a high utility of the protected system. We develop a general optimization problem that considers different kinds of model extraction attacks, and MODELGUARD provides an information-theoretic defense to efficiently solve the optimization problem and achieve resistance against adaptive attacks. Experiments show that MODELGUARD attains significantly better defensive performance against adaptive attacks with less loss of utility compared to previous defenses. © USENIX security Symposium *** rights reserved.

关键词： Differential privacy

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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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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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Genetic Algorithm-optimized k-nearest Neighbors and Support Vector Machines for Breast Cancer Detection in Resource-constrained Environments

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第3期52卷 848-861页

作者： Alemu, Abebe Girma, Anteneh Abebe, Mesfin Srinivasagan, Ramasamy Adama Science and Technology University Adama Ethiopia Computer Science/Cyber-Security department University of the District of Columbia WashingtonDC United States Computer Science and Engineering department Adama Science and Technology University Adama Ethiopia Computer Engineering department CCSIT King Faisal University Al Hufuf Saudi Arabia

Breast cancer poses a significant global threat, highlighting the urgent need for early detection to reduce mortality rates. Researchers are working to minimize the occurrence of false positives and false negatives, thereby improving the efficiency of breast cancer detection models. To achieve this, they employ advanced techniques such as artificial intelligence, machine learning, deep learning, and computational intelligence. Support vector machines (SVM) and k-nearest neighbors (KNN) are two popular lightweight machine-learning techniques.;however, their effectiveness depends on proper feature selection and parameter tuning. Genetic algorithm optimization provides a solution by intelligently selecting relevant features and fine-tuning parameters, which enhances classification accuracy for early diagnosis. This study demonstrates the effectiveness of a hybrid computational intelligence model that utilizes genetic algorithms for feature selection. The proposed GAKNN-SVM model shows superior performance in detecting breast tumors, utilizing the Wisconsin Breast Cancer Diagnostic Dataset. The results indicate significant improvements, with accuracy, sensitivity, and specificity rates reaching 98.25%, 98.15%, and 98.41%, respectively, based on 171 test samples. Overall, genetic algorithms and machine learning approaches hold great promise for improving breast cancer detection accuracy, ultimately leading to better diagnostic outcomes and reduced mortality rates, especially in resource-constrained environments. © (2025), (International Association of Engineers). All rights reserved.

关键词： Support vector machines

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Malicious PE File Detection Using Machine Learning: An Analysis of Header Features

Malicious PE File Detection Using Machine Learning: An Analy...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Shyam Kumar, S. Shetty, Jyothi Department of Cyber Security Nitte574 110 India Department of Computer Science and Engineering Nitte574 110 India

ISBN: (纸本)9798331517892

Portable Executable (PE) files serve as the foundation of the Windows operating system, containing executable code, object code, and Dynamic Link Libraries (DLLs). Their importance lies in their capacity to execute tasks and run programs within the Windows environment, rendering them essential to software execution. Comprehension of PE files is crucial in cybersecurity due to their prevalence and potential for harboring malicious software. Analysis of PE files enables researchers and analysts to uncover insights into their structure, behavior, and dependencies, which is essential for detecting and categorizing potential threats. PE file analysis provides critical information about a program's characteristics, facilitating the identification of indicators of compromise and potential security risks. Through examination of PE headers, sections, and attributes, analysts can gain insights into the file's functionality, dependencies, and potential security vulnerabilities. This knowledge equips cybersecurity professionals to develop robust detection and mitigation strategies, safeguarding systems and networks against cyber threats. In this paper, we examine the intricate details of PE files, utilizing tools such as PE-bear, PEinfo, CFF Explorer, and PEStudio to conduct comprehensive analysis. Our study employs machine learning techniques, specifically the Random Forest algorithm, to enhance malware detection capabilities. By preprocessing and augmenting the dataset with new executable files, we aim to enhance the efficacy of our analysis and provide valuable insights for cybersecurity research and defense strategies. © 2024 IEEE.

关键词： cybersecurity

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EEG-VARNet: An Advanced Deep Learning Model for AccuratePrediction and Classification of Major Depressive Disorder Using EEG Data 6

EEG-VARNet: An Advanced Deep Learning Model for AccuratePred...

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6th International Conference on cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Mahender, Udutala Sathiyasuntharam, V. Annamalai University Department of Computer Science and Engineering Tamilnadu Chidambaram India CMR Engineering College Department of Cyber Security Hyderabad India

ISBN: (纸本)9798331505790

The correct and early identification of Major Depressive Disorder (MDD) is challenging because of its complicated causes and symptoms that are similar to those of other mental diseases. This study introduces a new method for forecasting and categorizing MDD by utilizing EEG data, with a specific emphasis on Valence and Arousal measurements. Our approach combines sophisticated methods for extracting and selecting features with deeplearning models. Firstly, EEG signals are subjected to preprocessing in order to eliminate artifacts and standardize the quality of the data. The Boruta algorithm is utilized to extract features, which are then selected using the improved Cuckoo Search Algorithm. The suggested model, referred to as EEG-VARNet, utilizes two CNN models (Xception V4) and an SVM classifier to capture both spatial and temporal dependencies present in the EEG data. The model undergoes hyperparameter adjustment to maximize performance. The results of our study show substantial enhancements in the prediction of MDD with an accuracy rate of 9 8. 2 %, a precision rate of 96.43 %, and a recall rate of 9 7. 8 9 %. The measurements demonstrate the model's exceptional ability to diagnose MDD, emphasizing its promise as a dependable tool for clinical use. © 2024 IEEE.

关键词： Contrastive Learning

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