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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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LCEFL: A Lightweight Contribution Evaluation Approach for Federated Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6643-6657页

作者： Guo, Jingjing Li, Jiaxing Liu, Zhiquan Xiong, Yupeng Ma, Yong Vasilakos, Athanasios V. Li, Xinghua Ma, Jianfeng Xidian University School of Cyber Engineering Xi’an710071 China Jinan University College of Cyber Security Guangzhou510632 China Jiangxi Normal University Department of Computer Science and Technology Nanchang330022 China Department of Networks and Communications College of Computer Science and Information Technology Dammam31441 Saudi Arabia Grimstad4879 Norway

The prerequisite for implementing incentive mechanisms and reliable participant selection schemes in federated learning is to obtain the contribution of each participant. Available evaluation methods for participant contributions require the server to possess a test dataset, often impractical. Additionally, the excessively high complexity of these works is unacceptable when training complex models in large-scale federated learning system. To address these issues, we propose a lightweight contribution evaluation method for federated learning participants, named LCEFL, based on model projection theory, which does not require the server to provide a test dataset. In addition, a model compression method is designed to be used in LCEFL to reduce the computational complexity. Furthermore, a trusted aggregation method based on LCEFL is proposed, where the weight of each participant's local model is determined by its trust level, which can be calculated using its contribution evaluation result. Experimental results show that LCEFL can achieve nearly the same accuracy as schemes based on Shapley Value, while significantly reducing computational overhead by more than 50%. Compared to available aggregation methods, the proposed trusted aggregation scheme is able to accelerate the convergence speed of the global model and improve its accuracy by 2% to 45%. © 2002-2012 IEEE.

关键词： Federated Learning Computational Modeling Accuracy Servers Training Data Models Machine Learning Mobile Computing Particle Measurements Convergence Federated Learning Contribution Evaluation Trust Evaluation Model Projection Theory Secure Aggregation Federated Learning Computational Complexity Test Dataset Global Model Selection Of Participants Model Projections Aggregation Method Shapley Value Speed Of The Model Contributions Of Participants Number Of Participants Accuracy Of Model Computational Cost Local Data Learning Task Layer Model Participation Outcomes Updated Model Value Of Participation Model Aggregation Server Aggregates Global Convergence Iterative Rounds Trust Value Local Updates MNIST Dataset Total Amount Of Data Local Training Aggregation Algorithm Complex Model Structure

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Neural Network-Based State Estimation for Nonlinear Systems With Denial-of-Service Attack Under Try-Once-Discard Protocol

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2182-2184页

作者： Xueli Wang Shangwei Zhao Ming Yang Xin Wang Xiaoming Wu the Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan 250014 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250014China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)*** specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement *** addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.

关键词： Service DoS service

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A power consumption prediction algorithm based on job similarity judgment 8

A power consumption prediction algorithm based on job simila...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Yang, Meihong Chen, Yongjie Wang, Jibin Guo, Ying Qin, Shuwei Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Intelligent Public Security Joint Innovation Center Department of Public Security of Shandong Province Jinan China

ISBN: (纸本)9798350302455

Facing the high energy consumption problem of High Performance Computing(HPC) clusters, power consumption prediction of HPC clusters can help data centers improve energy efficiency and formulate energy-saving strategies. However, the existing power prediction methods predict the power consumption of many different jobs in an HPC cluster, and due to the variety of job types in the cluster, the power consumption of different types of jobs varies, and even the same job may change due to different parameters, thus reducing the accuracy of the prediction. To address the current problem of low accuracy of power consumption prediction for multiple jobs in HPC clusters, we propose a power consumption prediction method(PCSSJ). based on the similarity judgment of user jobs The algorithm consists of two modules: similarity judgment and power consumption prediction. The algorithm can select a set of jobs similar to the target job from the historical jobs executed by this user for training and predict the target job. To verify the effectiveness of the PCPSJ algorithm, we train on five different types of datasets and compare them with the baseline model, and the results show the superiority of the PCPSJ algorithm. © 2023 IEEE.

关键词： Electric power utilization

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Neuroadaptive Incentivization in Healthcare using Blockchain and IoT

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SN computer Science 2024年第1期5卷 13页

作者： Kumar, Ajay Singh, Rajiv Ranjan Chatterjee, Indranath Sharma, Nikita Rana, Vaishali Department of Computer Science and Informatics Central University of Himachal Pradesh Himachal Pradesh Dharamshala India Department of Cyber Security and Networks Glasgow Caledonian University Glasgow Scotland United Kingdom Department of Computer Engineering Tongmyong University Busan South Korea School of Technology Woxsen University Hyderabad India

Financially incentivizing health-related behaviors can improve health record outcomes and reduce healthcare costs. Blockchain and IoT technologies can be used to develop safe and transparent incentive schemes in healthcare. IoT devices, such as body sensor networks and wearable sensors, etc. connect the physical and digital world making it easier to collect useful health-related data for further analysis. There are, however, many security and privacy issues with the use of IoT. Some of these IoT security issues can be alleviated using Blockchain technology. Incorporating neuroadaptive technology can result in more personalized and effective therapies using machine learning algorithms and real-time feedback. The research investigates the possibilities of neuroadaptive incentivization in healthcare using Blockchain and IoT on patient health records. The core idea is to incentivize patients to keep their health parameters within standard range thereby reducing the load on healthcare system. In summary, we have presented a proof of concept for neuroadaptive incentivization in healthcare using Blockchain and IoT and discuss various applications and implementation challenges. © 2023, The Author(s).

关键词： Blockchain Healthcare IoT Neuroadaptive Smart contracts

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An Unmanned Aerial Vehicle as a Multi-State System 16

An Unmanned Aerial Vehicle as a Multi-State System

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16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer Engineering, TCSET 2022

作者： Kliushnikov, Ihor Kharchenko, Vyacheslav Fesenko, Herman Department of Computer Systems Networks and Cyber Security National Aerospace University 'KhAI' Kharkiv Ukraine

ISBN: (纸本)9781665468619

An unmanned aerial vehicle (UAV) is considered as a multi-state system (MSS). The results of the decomposition of the UAV into degradable and non-degradable components are presented. The reliability models of UAV as MSS have been developed and researched. Dependencies are obtained take into account the possible degradation levels of the UAVs various equipment. An example of the developed models application for evaluating the reliability of UAV-based monitoring system that collects two types of data from several target points is given. The results obtained can be used to assess the UAV and UAV fleets dependability and survivability considering complex physical and informational conditions. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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IoT-Enhanced Gesture Recognition System for Healthcare Applications

IoT-Enhanced Gesture Recognition System for Healthcare Appli...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Krithika, L.B. Nallakaruppan, M.K. Baza, Mohamed Badr, Mahmoud Deora, Yuvraj Singh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India College of Charleston Deparment of Computer Science CharlestonSC United States Polytechnic Institute Department of Networks and Computer Security UticaNY12201 United States

ISBN: (纸本)9798350351408

In response to the prevalent language barriers in medical settings, this paper proposes an innovative solution leveraging gesture recognition technology. Through the integration of deep learning models and Internet of Things (IoT) tools, the system aims to facilitate seamless communication between patients and healthcare providers. Initially, a deep learning model is developed using a personalized dataset containing various medical gestures, allowing for the recognition of nuanced patient expressions such as 'I have Fever' or 'Shoulder pain.' Additionally, the system incorporates IoT sensors to monitor vital signs like body temperature and pulse in real-time, ensuring comprehensive patient assessment. With the capability to continuously evolve and adapt to new gestures, this solution not only enhances accessibility but also improves the quality of healthcare delivery, particularly in underserved communities. By bridging the gap between language barriers and medical communication, this innovative approach holds promise for fostering inclusive and effective healthcare practices worldwide. In addressing the challenges posed by language barriers in medical interactions, this paper presents a comprehensive solution that combines gesture recognition technology with deep learning models and IoT devices. By capturing and interpreting patient gestures, such as indicating specific symptoms or discomfort, the system facilitates clear communication between patients and doctors. Furthermore, the integration of IoT sensors enables continuous monitoring of vital signs, allowing for timely interventions and personalized care. With its ability to adapt and expand to accommodate new gestures and medical scenarios, this solution represents a significant advancement in healthcare accessibility and quality. By leveraging cutting-edge technology, this approach has the potential to revolutionize healthcare delivery, particularly in areas with limited access to linguistic resources or medi

关键词： Gesture recognition

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Extensive Study of Cloud Computing Technologies, Threats and Solutions Prospective

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computer Systems Science & Engineering 2022年第4期41卷 225-240页

作者： Mwaffaq Abu-Alhaija Nidal M.Turab AbdelRahman Hamza Department of Networks and Information Security Faculty of Information TechnologyAl-Ahliyya Amman UniversityAmman19328Jordan Department of Computer Science Information TechnologyAl-Ahliyya Amman UniversityAmman19328Jordan

Infrastructure as a Service(IaaS)provides logical separation between data,network,applications and machines from the physical constrains of real *** is one of the basis of cloud ***,security issues are also gradually emerging with virtualization of cloud *** security aspects of cloud virtualization will be explored in this research paper,security recognizing potential threats or attacks that exploit these vulnerabilities,and what security measures are used to alleviate such *** addition,a dis-cussion of general security requirements and the existing security schemes is also *** shown in this paper,different components of virtualization environ-ment are targets to various attacks that in turn leads to security issues compromis-ing the whole cloud *** this paper an overview of various cloud security aspects is also *** attack scenarios of virtualization envir-onments and security solutions to cater these attacks have been discussed in the *** then proceed to discuss API security concerns,data security,hijacking of user account and other security *** aforementioned discussions can be used in the future to propose assessment criteria,which could be useful in ana-lyzing the efﬁciency of security solutions of virtualization environment in the face of various virtual environment attacks.

关键词： Cloud computing environment paravirtualization full virtualization cloud virtualization security hypervisor virtual machines

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A bytecode-based integrated detection and repair method for reentrancy vulnerabilities in smart contracts

IET Blockchain

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IET Blockchain 2024年第3期4卷 235-251页

作者： Feng, Zijun Feng, Yuming He, Hui Zhang, Weizhe Zhang, Yu School of Cyberspace Science Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of New Networks Peng Cheng Laboratory Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China

The reentrancy vulnerability in smart contracts has caused significant losses in the digital currency economy. Existing solutions for detecting and repairing this vulnerability are limited in scope and lack a comprehensive framework. Additionally, there is currently a lack of guidance methods for effectively pinpointing the location of vulnerabilities. The proposed bytecode-level method addresses these challenges by incorporating a detection module, an auxiliary localization module, and a repair module. An opcode classification method is introduced using vulnerability features and a BiLSTM-Attention-based sequence model to enhance detection accuracy. To overcome difficulties in vulnerability localization, an auxiliary localization method based on data flow and control flow analysis is proposed, enabling developers to better locate vulnerabilities. Current reentrancy vulnerability repair methods are analyzed and strategies for three reachable patterns are proposed. The bytecode rewriting strategy utilizes Trampoline technology for repair, while a fuel optimization method reduces bytecode generation length to optimize gas costs. Through extensive experimental validation, the effectiveness and superiority of the proposed methods are confirmed, further validating the feasibility of the entire framework. Experimental results demonstrate that the framework offers enhanced protection against reentrancy vulnerability attacks in smart contracts. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Smart contract

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Traffic Congestion Prediction Using Machine Learning: Amman City Case Study

Traffic Congestion Prediction Using Machine Learning: Amman ...

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2024 International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors, MIEITS 2024

作者： Arabiat, Areen Hassan, Mohammad Al Momani, Omar Department of Communication and Computer Engineering Faculty of Engineering Al Ahliyya Amman University Amman19111 Jordan Department of Networks security Faculty of Information Technology The World Islamic Sciences and Educations University WISE Amman Jordan

ISBN: (纸本)9781510680814

Traffic congestion leads to wasted time, pollution, and increased fuel usage. Traffic congestion prediction has become a developing research topic in recent years, particularly in the field of machine learning (ML). The evaluation of various traffic parameters is used to predict traffic congestion by relying on historical data. In this study, we will predict traffic congestion in Amman city specifically at Northbound Street at 8th Circle using different ML classifiers. The 8th Circle links four main streets: Westbound, Northbound, Eastbound, and Southbound. Because of the heavy traffic on Northbound, it was chosen to predict traffic congestion. Datasets were collected from the greater Amman municipality hourly based. Datasets were divided and per-process to be understandable. The Logistic Regression, Decision Tree (DT), Random Forest, and multilayer perceptron (MLP) classifiers have been chosen to predict traffic congestion at Northbound Street linked with the 8th Circle. Four experiments were run under The Waikato Environment for Knowledge Analysis (WEKA) tools to find the best classifiers to predict traffic congestion at Northbound. The chosen classifiers have been evaluated using F-Measure, sensitivity, precision, and accuracy evaluation metrics. The obtained results from all experiments have demonstrated the Logistic Regression is the best classifier to predict traffic congestion. The accuracy of Logistic Regression to predict traffic congestion at Northbound Street was97.6%. © 2024 SPIE.

关键词： Traffic congestion

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