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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ahmed, Alaa M. Saeed, Mogeeb A. Hamood, Amr A. Alazab, Abduljabar A. Ahmed, Kamel A. Computer Networks & Distributed Systems Al-saeed It Taiz University Faculty of Engineering Taiz Yemen Taiz University Al-saeed Faculty of Engineering & It Computer Networks & Distributed Systems Department Taiz Yemen

ISBN: (纸本)9798350305333

Android currently dominates the smartphone market, accounting for an impressive market share of over 70%. However, because of its widespread acceptance, mobile operating systems have become a prime target for bad actors looking to profit from them. Particularly Android has been subjected to an increasing barrage of malware assaults, including the infamous Android Banking Trojans. This study investigates the effectiveness of static analysis in locating Android banking malware in order to counter this threat. It does so by utilizing a wide range of features, including permissions, application programming interface (API) calls, opcodes, API packages, system commands, intents, strings, services, receivers, and activities. The study suggests using machine learning techniques to assess the detection of Android malware by utilizing various sets of classifiers in order to achieve this goal. The study also uses a feature selection approach to determine which features are most useful for telling malicious code apart from good code. 500 samples of malicious code and 500 samples of benign code make up the dataset that was used. The XGboost algorithm outperforms others in terms of accuracy, achieving an impressive accuracy value of 99.5% in malware detection after conducting a thorough comparison of various classifier sets. These results demonstrate the potential of static analysis and machine learning as useful tools in fending off the growing threats posed by Android malware. © 2023 IEEE.

关键词： Android malware

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Comments on 'VERSA: Verifiable Secure Aggregation for Cross-Device Federated Learning'

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IEEE Transactions on Dependable and Secure Computing 2024年第1期21卷 499-500页

作者： Luo, Fucai Wang, Haiyan Yan, Xingfu Zhejiang Gongshang University College of Computer and Information Engineering Hangzhou310000 China Peng Cheng Laboratory Department of New Networks Shenzhen518066 China South China Normal University School of Computer Science Guangzhou510631 China

Federated learning (FL) allows a large number of users to collaboratively train machine learning (ML) models by sending only their local gradients to a central server for aggregation in each training iteration, without sending their raw training data. The main security issues of FL, that is, the privacy of the gradient vector and the correctness verification of the aggregated gradient, are gaining increasing attention from industry and academia. To protect the privacy of the gradient, a secure aggregation was proposed;to verify the correctness of the aggregated gradient, a verifiable secure aggregation that requires the server to provide a verifiable aggregated gradient was proposed. In 2021, Hahn et al. proposed VERSA, a verifiable secure aggregation. However, in this article, we will point out a flaw in VERSA, which indicates that VERSA does not work. To address the flaw, we present several approaches with different advantages and disadvantages. We hope that by identifying the flaw, similar errors can be avoided in future designs of verifiable secure aggregation. © 2004-2012 IEEE.

关键词： Learning systems

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Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

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Agent Selection Framework for Federated Learning in Resource-Constrained Wireless networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1265-1282页

作者： Raftopoulou, Maria Da Silva, Jose Mairton B. Litjens, Remco Vincent Poor, H. Van Mieghem, Piet Uppsala University Department of Information Technology Uppsala751 05 Sweden Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands Department of Networks The Hague2595 DA Netherlands Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Federated learning is an effective method to train a machine learning model without requiring to aggregate the potentially sensitive data of agents in a central server. However, the limited communication bandwidth, the hardware of the agents and a potential application-specific latency requirement impact how many and which agents can participate in the learning process at each communication round. In this paper, we propose a selection metric characterizing each agent's importance with respect to both the learning process and the resource efficiency of its wireless communication channel. Leveraging this importance metric, we formulate a general agent selection optimization problem, which can be adapted to different environments with latency or resource-oriented constraints. Considering an example wireless environment with latency constraints, the agent selection problem reduces to the 0/1 Knapsack problem, which we solve with a fully polynomial approximation. We then evaluate the agent selection policy in different scenarios, using extensive simulations for an example task of object classification of European traffic signs. The results indicate that agent selection policies which consider both learning and channel aspects provide benefits in terms of the attainable global model accuracy and/or the time needed to achieve a targeted accuracy level. However, in scenarios where agents have a limited number of data samples or where the latency requirement is very stringent, a pure learning-based agent selection policy is shown to be more beneficial during the early or late stages of the learning process. © 2023 CCBY.

关键词： Federated learning

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A Reinforcement Learning Approach for Routing in Marine Communication Network of Fishing Vessels

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SN computer Science 2025年第1期6卷 1-12页

作者： Surendran, Simi Montresor, Alberto Vinodini Ramesh, Maneesha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Information Engineering and Computer Science University of Trento Trento Italy Amrita Center for Wireless Networks & Applications (AmritaWNA) Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kollam India

The lack of affordable communication facilities to the shore remains a fundamental problem for fishermen engaged in deep-sea fishing. The Offshore Communication Network (OCN) is a wireless network of fishing vessels, whose goal is to provide Internet over the ocean. However, the dynamic nature of OCNs characterized by extreme weather, the difficulty of deploying additional infrastructure, wave-induced vessel movements, and high mobility causes significant challenges for traditional routing protocols. This paper proposes OCN-AR, a Q-learning-based adaptive routing strategy for ocean networks. The quality of the learning process relies on the reward function, which has been carefully designed to incorporate the most important features, including real-time forecasts of connectivity quality, path probability, link availability duration, and distance to the destination. The routing performance is evaluated through extensive simulations conducted under diverse conditions, including varying mobility scenarios, transmission rates, vessel rocking intensities, and node densities, and is compared against traditional protocols. The results demonstrate that OCN-AR significantly outperforms existing routing approaches, making it a reliable solution for maritime communication. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fishing vessel network Marine communication network Reinforcement learning Routing protocol

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Control of Grid-Connected Inverters Using PLL for Synchronization and Harmonic Mitigation

Control of Grid-Connected Inverters Using PLL for Synchroniz...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Yousefpour, Kamran Yousefpour, Mohammad Molla, Seyyed Javad Hosseini Andani, Mansour Taheri Varnousdaderani, Sepideh Alaei Andani, Hamid Taheri Mississippi State University Department of Electrical and Computer Engineering United States Azad University Iran Babol Noshirvani University of Technology Department of Electrical and Computer Engineering Iran School of Manufacturing Systems and Networks Arizona State University United States Azad University Department of Computer Engineeering Iran Isfahan University of Technology Department of Electronic Engineering Iran

ISBN: (纸本)9798331541125

This paper presents the design and simulation of a single-phase grid-connected inverter control system, focusing on enhancing power quality and dynamic performance. The control system comprises a resonant controller for real and reactive power regulation, ensuring sinusoidal current injection into the grid. Additionally, a Phase-Locked Loop (PLL) is incorporated to improve synchronization with the grid voltage, generating accurate reference signals for current control and mitigating harmonic disturbances. The PLL design leverages specific gain parameters, μ1,μ2, and μ3, optimized for a critically damped response. A comprehensive simulation study evaluates the system's performance under different conditions, including soft-start operation, transient power commands, and grid voltage distortion with low-order harmonics (Sth, 7th, LIth, and 13th). Results highlight the system's robust dynamic response, low total harmonic distortion (THD), and effective harmonic attenuation, demonstrating its suitability for grid-connected distributed energy resource (DER) applications. This work provides a scalable and reliable control framework for next-generation inverter systems operating in modern, complex grid environments. © 2025 IEEE.

关键词： Harmonic distortion

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Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 512-523页

作者： Jie Chen Kaiyi Luo Changbing Tang Zhao Zhang Xiang Li the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004 Yuxi Middle School Taizhou 317399China IEEE the College of Physics and Electronics Information Engineering Zhejiang Normal UniversityJinhua 321004China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004China the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433 the Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 201210China

Weighted vertex cover(WVC)is one of the most important combinatorial optimization *** this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted *** first model the WVC problem as a general game on weighted *** the framework of a game,we newly define several cover states to describe the WVC ***,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the ***,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial ***,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the ***,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.

关键词： Game-based asynchronous algorithm(GAA) game optimization polynomial time strict Nash equilibrium(SNE) weighted vertex cover(WVC)

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Power Control for Collaborative Sensors in Internet of Things Environments Using K-means Approach

Power Control for Collaborative Sensors in Internet of Thing...

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International Conference on Applied Soft Computing and Communication networks, ACN 2023

作者： Hasan, Mohammed Zaki Alabady, Salah Abdulghani Salleh, Mohd Fadzli Mohd Computer Networks Department College of Computer Science and Mathematics University of Mosul Mosul Iraq Computer Engineering Department College of Engineering University of Mosul Mosul Iraq School of Electrical and Electronic Engineering Universiti Sains Malaysia Seri Ampangan Pulau Pinang Nibong Tebal14300 Malaysia

ISBN: (纸本)9789819720033

Energy efficiency in sensor networks is a critical concern, as sensors are often resource-constrained devices with limited battery power. Maximizing energy efficiency is essential to prolonging the network’s lifetime, reducing maintenance costs, and enabling long-term monitoring and data collection in various Internet of Things (IoT) applications. Collaborative beamforming involves multiple sensors that coordinate their signal transmission to be focused on a specific direction. However, sensors may have various energy budget limitations to achieve beamformed signal transmission in a specific direction of the sink. In this paper, we developed a power control approach of beamforming transmission to prolong the lifetime of sensors by organizing the collaborative sensors into clusters. The proposed algorithm is based on the K-means clustering algorithm, which groups these collaborative sensors into clusters based on their residual energy information (REI). REI is changed among collaborative sensors to communicate with each other within a cluster. Therefore, each of these collaborative sensors adjusts its transmission power level to achieve balance in making decisions and facilitate collaboration between sensors in the inner cluster. Simulation results demonstrate that the proposed algorithm requires significantly low overhead and computational complexity in terms of prolonging the lifetime of the collaborative sensor cluster as compared to alternative power control algorithm that balances the energy consumption of the entire network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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Nanorobot-Based Intelligent Symptoms Analysis and Recommendation Framework in Edge networks

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-8页

作者： Nandy, Sudarshan Hazra, Abhishek Adhikari, Mainak Puthal, Deepak School of Computer Science and Engineering SCOPE VIT University Vellore Tamil Nadu India Communications & Networks Lab National University of Singapore Singapore Department of Computer Science IIIT Lucknow Lucknow India Department of EECS Khalifa University UAE

Nanorobots are microscopic robots that operate at the molecular and cellular level and can potentially revolutionize fields such as medicine, manufacturing, and environmental monitoring due to their precision. However, the challenge for researchers is to analyze the data and provide a constructive recommendation framework instantly, as most nanorobots demand on-time and near-edge processing. To tackle this challenge, this research presents a novel edge-enabled intelligent data analytics framework called Transfer Learning Population Neural Network (TLPNN) to predict glucose levels and associated symptoms from invasive and non-invasive wearable devices. The TLPNN is designed to be unbiased in predicting symptoms during the initial phase but later modified based on the best-performing neural networks during the learning phase. The effectiveness of the proposed method is validated using two publicly available glucose datasets with various performance metrics. The simulation results demonstrate the effectiveness of the proposed TLPNN method over existing ones. IEEE

关键词： Neural networks

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Careful Resume: Design and Analysis with Picoquic over Satellite Paths 12

Careful Resume: Design and Analysis with Picoquic over Satel...

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12th Advanced Satellite Multimedia Systems Conference and the 18th Signal Processing for Space Communications Workshop, ASMS/SPSC 2025

作者： Hofstatter, Matthias Deutschmann, Jörg Secchi, Raffaello Fairhurst, Gorry German, Reinhard Friedrich-Alexander-Universität Erlangen-Nürnberg Computer Networks and Communication Systems Germany University of Aberdeen Department of Engineering United Kingdom

ISBN: (纸本)9798331522353

Slow Start increases the congestion window by doubling each round trip until congestion is detected. As a result, reaching optimal speed typically takes several round trip times, especially important in high bandwidth-delay product networks. Geostationary satellite communications now use Performance Enhancing Proxies to accelerate the growth of the congestion window. However, these are ineffective for connections with encrypted headers or UDP traffic. We propose a new method, Careful Resume, that enables a rapid ramp-up of congestion control by reusing previous parameters. We implement Careful Resume in Picoquic and run performance tests using an emulated geostationary satellite path, three real geostationary satellite networks, the low Earth orbit satellite megaconstellation Starlink, and a cellular 5G network. Results confirm that Careful Resume accelerates the ramp-up process, reducing total transmission time. Even for connections with lower bandwidth-delay products, the network congestion point can be reached much more quickly. Benefits are particularly pronounced for paths with high bandwidth-delay products and medium-sized transfers such as web traffic. © 2025 IEEE.

关键词： Geostationary satellites

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