检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

An Integrated Framework with Enhanced Primitives for Post-Quantum Cryptography: HEDT and ECSIDH for Cloud Data Security and Key Exchange

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第11期49卷 165-176页

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Durga, V. Sathya Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Computer Science&Engineering Hindustan Institute of Technology and Science Chennai India

If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must be developed. Current PQC schemes primarily focus on data security or key exchange, and further improvement towards enhanced PQC primitives is required. Our proposal in this research is an innovative paradigm for PQC-focused cloud data security. The proposed HEDT approach achieves encryption and decryption with significantly lower latency (20% improvement) and higher reliability than AES, DES, and RSA, as demonstrated through experimental results. Furthermore, ECSIDH, a hybrid key exchange mechanism combining SIDH and ECDH, improves security strength by 50% while maintaining computational costs within 1.13x of SIDH. Compared to individual key exchange schemes like SIDH, ECSIDH offers superior security as a PQC candidate. These results confirm the robustness and efficiency of the proposed framework in ensuring secure data outsourcing and key exchange in cloud environments. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Design of real time intelligent system for women safety

引用

Recent Patents on engineering 2024年第3期18卷 77-83页

作者： Kohli, Priyanka Singh, Kawaljeet Sidhu, Brahmaleen Kaur Department of Computer Science & Engineering Punjabi University Patiala India University Computer Centre Punjabi University Patiala India

Background: Even the technology has advanced a lot in these modern times, women's safety is still a massive issue. Women are not safe anywhere. Their safety is an essential and decisive issue in today’s world. They face extreme troubles when they are alone, travelling on lonely roads and at workplace. Education is essential for everyone and especially for women. It is a powerful weapon to end poverty. Government and NGOs have made many rules and regulations but the crime against women has not ended. There are many instances of harassment, abuse, eve-teasing, kidnapping, domestic violence and rape. Women are working equally as men but still their safety is a big challenge. With the boom in the IT and corporate sector, many women are now working and even during nightshifts. Objectives: A Real Time Intelligent System for Women Safety has been proposed to overcome these limitations. Methods: In this paper, many women's safety systems that were proposed or developed have been discussed. Major drawbacks in the current system, devices or applications have been highlighted. Latest technologies like machine learning, artificial intelligence or data science have been ignored. Results: The proposed system will be highly sophisticated with the latest techniques and women will feel safe in all situations. It will predict the safe route while moving alone. Conclusion: Moreover, it will record the evidence and will also suggest the rights of women. © 2024 Bentham science Publishers.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Optimal Design of a Planning and Contracting Framework to Enable Vehicle to Building and Grid Services via Demand Response

引用

IEEE Transactions on Transportation Electrification 2025年第3期11卷 7542-7556页

作者： Elsayed, Ahmed Abd Elaziz Saxena, Shivam Farag, Hany E. Z. Electrical Engineering and Computer Science Department Lassonde School of Engineering York University Keele Campus Toronto ON Canada Department of Electrical and Computer Engineering University of New Brunswick Fredericton NB Canada

Bidirectional electric vehicle (EV) charging enables stored energy to reduce peak loads for buildings (V2B) and the grid (V2G). However, building owners investing in V2B infrastructure while generating revenue from V2G services face challenges in planning and coordinating with EV owners due to uncertainties in their schedules and profit-sharing expectations. Additionally, misaligned building and grid peak times can create conflicts between V2B and V2G goals, which may negatively impact the building electricity bill. Unlike previous studies that used a contract-free approach for aggregating V2B and V2G, resulting in inconsistent participation, this paper proposes a novel planning and contracting framework that enables building owner to determine the optimal contract parameters with EV owners. These parameters include minimum participation time in DR events, minimum arrival State of Charge (SoC), and permitted emergency departure hours. The framework supports V2B aggregation with on-site distributed energy resources for DR and V2G services, ensuring transparency and fairness through shared profits and performance-based penalties, while compensating building electricity bills due to V2G activities. The framework is composed of a tri-stage optimization process that uses Monte Carlo Simulations to generate EV owner profit assessments, select optimal EV candidates based on charger availability, estimate contract parameters with profit/penalty sharing, and assign contracts between power system operators, building owners, and EV owners under multiple virtual scenarios. Simulation results validate the contract parameter assessment and its performance via a three day case study with real-world data sets that demonstrates net revenue generation of $209 for each EV owner and $950 for building owners during DR events with a Return on Investment (ROI) of 134.5% 130.7% for the EVs and Building owners, respectively. © 2015 IEEE.

关键词： Vehicle-to-grid

来源：评论

学校读者我要写书评

暂无评论

Feature Extraction From Single-Channel EEG Using Tsfresh and Stacked Ensemble Approach for Sleep Stage Classification

引用

International Journal of e-Collaboration 2023年第3期19卷 1-20页

作者： Radhakrishnan, B.L. Ezra, Kirubakaran Jebadurai, Immanuel Johnraja Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering GRACE College of Engineering Thoothukudi India

The smart world under Industry 4.0 is witnessing a notable spurt in sleep disorders and sleep-related issues in patients. Artificial intelligence and IoT are taking a giant leap in connecting sleep patients remotely with healthcare providers. The contemporary single-channel-based monitoring devices play a tremendous role in predicting sleep quality and related issues. Handcrafted feature extraction is a time-consuming job in machine learning-based automatic sleep classification. The proposed single-channel work uses Tsfresh to extract features from both the EEG channels (Pz-oz and Fpz-Cz) of the SEDFEx database individually to realise a single-channel EEG. The adopted mRMR feature selection approach selected 55 features from the extracted 787 features. A stacking ensemble classifier achieved 95%, 94%, 91%, and 88% accuracy using stratified 5-fold validation in 2, 3, 4, and 5 class classification employing healthy subjects data. The outcome of the experiments indicates that Tsfresh is an excellent tool to extract standard features from EEG signals. © 2023 IGI Global. All rights reserved.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Deep reinforcement learning for online scheduling of photovoltaic systems with battery energy storage systems

引用

Intelligent and Converged Networks 2024年第1期5卷 28-41页

作者： Yaze Li Jingxian Wu Yanjun Pan the Department of Electrical Engineering University of ArkansasFayettevilleAR 72701USA the Department of Computer Science and Computer Engineering University of ArkansasFayettevilleAR 72701USA

A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather *** proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging *** scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action *** of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy *** new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and *** new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep *** online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

关键词： photovoltaic(PV) battery energy storage system(BESS) Markov decision process(MDP) deep deterministic policy gradient(DDPG)

来源：评论

学校读者我要写书评

暂无评论

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

引用

computers, Materials & Continua 2024年第9期80卷 3485-3506页

作者： Tajim Md.Niamat Ullah Akhund Waleed M.Al-Nuwaiser Department of Computer Science and Engineering(CSE) Daffodil International UniversityDhaka1216Bangladesh Graduate School of Science and Engineering Saga UniversitySaga8408502Japan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)*** distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter *** improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and *** study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among *** Trees and Random Forests exhibited stable performance throughout the *** enhancing accuracy,hyperparameter optimization also led to increased execution *** representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular *** research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

关键词： Internet of sensing things(IoST) machine learning hyperparameter optimization cardiovascular disease prediction execution time analysis performance analysis wilcoxon signed-rank test

来源：评论

学校读者我要写书评

暂无评论

IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

引用

Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach to Industrial Corrosion Detection

引用

computers, Materials & Continua 2024年第11期81卷 2587-2605页

作者： Mehwash Farooqui Atta Rahman Latifa Alsuliman Zainab Alsaif Fatimah Albaik Cadi Alshammari Razan Sharaf Sunday Olatunji Sara Waslallah Althubaiti Hina Gull Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information Systems(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

关键词： Deep learning YOLOv8 EfficientNetB0 CNN corrosion detection Industry 4.0 sustainability

来源：评论

学校读者我要写书评

暂无评论

A Novel Deep Learning Approach Toward Efficient and Accurate Recommendation Using Improved Alternating Least Squares in Social Media

引用

Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 657-675页

作者： Dhawan, Sanjeev Singh, Kulvinder Batra, Amit Choi, Anthony Choi, Ethan Department of Computer Science and Engineering University Institute of Engineering and Technology Kurukshetra University Haryana India Department of Electrical and Computer Engineering Mercer University MaconGA United States

The increasing number of electronic transactions on the Internet has given rise to the design of recommendation systems. The main objective of these systems is to give recommendations to the users about the items (i.e., movies, music, books, etc.) they are likely to purchase in near future. These recommendations are on the foundation of the response provided by the other individuals about these products in the terms of ratings, which are in the form of a five-star rating system. Plenty of algorithms have been proposed in the past for building recommender systems. The most popular of these algorithms comprise content-centered and collaborative filtering recommendation methodologies. Recently, matrix factorization has appeared as a technique in recommender systems. Several algorithms centered on matrix factorization have been suggested in the recent past. In this paper, an improved alternating least squares algorithm has been proposed for building more efficient and accurate recommender systems which improves the performance of matrix factorization. The performance of the model has been evaluated using metrics like root-mean-square-error and execution time. However, in general, the time complexity of both algorithms is O(n^3), where n is the number of users or items. This is because both IALS and ALS involve iteratively solving a system of linear equations, and the time complexity of solving a system of linear equations is O(n^3). However, IALS can achieve significant performance improvements over ALS, particularly for large datasets, due to its incorporation of stochastic gradient descent and parallelization techniques. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：