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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

SN computer science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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Design and Optimization of Adaptive Network Coding Algorithms for Wireless Networks 15

Design and Optimization of Adaptive Network Coding Algorithm...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Nadeem, Syed Mohammed Rao, Deepak Dasaratha Arora, Arpit Dongre, Yashwant V. Giri, Rakesh Kumar Jaison, B. G Pulla Reddy Engineering College Department of Computer Science and Engineering India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India College Sunrise University Department Computer Science & Engineering Alwar301028 India R.M.K.Engineering College Department Computer Science and Engineering Kavarapettai601206 India

ISBN: (纸本)9798350370249

This paper specializes in the layout and optimization of adaptive community coding algorithms for WiFi networks. The goal of those algorithms is to maximize the performance of network transmissions at the same time as heading off collisions and losses. We analyze the performance of numerous current network coding algorithms, examine their strengths and weaknesses and advocate techniques to optimize their performance. We also increase and compare new algorithms based on the adaptive network coding method. Those algorithms are designed if you want to mechanically adapt to the environment and take full benefit of the available assets to maximize the overall network's performance. The results of our simulations endorse that the proposed adaptive network coding algorithms outperform conventional network coding algorithms in phrases of both throughput and latency. We further propose an optimized model of our adaptive network coding algorithms and evaluate its performance to that of the unique algorithms. Our results demonstrate significant performance profits in terms of both throughput and latency. © 2024 IEEE.

关键词： Algorithms Latency Optimization Throughput Transmissions

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Recent Advances in Single-Atom Catalysts for Photoelectrocatalytic Water Splitting

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Carbon Energy 2025年第4期7卷 114-142页

作者： Jiao Yang Xiaoyang Zheng Syed Shoaib Ahmad Shah Chao Wang Xueyao Li Zhishuo Yan Lishan Peng Key Laboratory of Rare Earths Ganjiang Innovation Academy Chinese Academy of Sciences Institute of Applied Physics and Materials Engineering University of Macau Department of Mechanical Engineering Graduate School of Engineering The University of Tokyo Department of Chemistry School of Natural Sciences National University of Sciences and Technology Department of Chemistry University of Connecticut School of Rare Earths University of Science and Technology of China Department of Electrical and Computer Engineering North Dakota State University

Hydrogen is a highly promising energy carrier because of its renewable and clean qualities. Among the different methods for H2production, photoelectrocatalysis（PEC） water splitting has garnered significant interest, thanks to the abundant and perennial solar energy. Single-atom catalysts（SACs）, which feature well-distributed atoms anchored on supports, have gained great attention in PEC water splitting for their unique advantages in overcoming the limitations of conventional PEC ***, we comprehensively review SAC-incorporated photoelectrocatalysts for efficient PEC water splitting. We begin by highlighting the benefits of SACs in improving charge transfer, catalytic selectivity, and catalytic activity, which address the limitations of conventional PEC reactions. Next, we provide a comprehensive overview of established synthetic techniques for optimizing the properties of SACs, along with modern characterization methods to confirm their unique structures. Finally, we discuss the challenges and future directions in basic research and advancements, providing insights and guidance for this developing field.

关键词： hydrogen production photoelectrocatalysis single-atom catalysts water splitting

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Space robotics hybrid conceptual model for tracking and estimation using IoRT and AI-based control system

Space robotics hybrid conceptual model for tracking and esti...

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作者： Sinha, Anurag Garg, Namit Singh, N.K. Kumar, Biresh Banerjee, Pallab Verma, Achyut Kumar Department of Computer Science IGNOU Delhi India Department of Mathematics and Computing Indian Institute of Technology Kanpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India Department of Computer Science and Engineering Amity University Jharkhand Ranchi Jharkhand India Department of Chemical Engineering Indian Institute of Technology Kanpur India

Robotics is an amalgamation of mechanical engineering and computer science. Mechanical engineering helps to design and develop mechanical parts and devices for control systems in robots. Space robots and robotics are known as devices that can enhance the manipulation, functionalities, and control exercised by astronauts, and thus can be called their artificial assistants for real-time exploration of space conditions. Control systems in robots like gestures and action become predominant for control, thus human-robot interaction comes into existence. AI and reinforcement learning have been used to control functionality in robots in various fields, but IoRT (Internet of robotic Things) is an emerging area of IoT that has the capabilities to monitor a variety of courses of action in robots. In this paper, we are proposing a conceptual framework based on an IoRT control system integrated with AI and reinforcement learning algorithms, which has been accessed based on multiple papers in the same domain, and it will help future researchers develop and simulate such a prototype. We are also using AKF (Adaptive Kalman Filtering) for robot tracking and noise deduction in sensors integrated with the A* algorithm. Overall, this is a concept-based framework to be designed and simulated. © 2023

关键词： Reinforcement learning

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Empowering Early Detection: CervicalCareXG Revolutionizes Cervical type cancer Diagnostics 4

Empowering Early Detection: CervicalCareXG Revolutionizes Ce...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Verma, Shanti Suthar, Hiral Rami, Khyati L J University Department of Computer Applications Gujarat Ahmedabad India Silver Oak University Department of Computer Applications Gujarat India Sal Institute of Technology & Engineering Research Department of Computer Science Gujarat India

ISBN: (纸本)9798350354218

A XGBoost-Based System in Cervical type cancer Diagnostics are proposed in the current research work. Generally Cervical type of cancer will be a significant health concern worldwide, particularly in the regions with limited access on advanced healthcare facilities. On Early detection will plays a pivotal role on improving patient outcomes and by reducing mortality rates. In this study, the propose system termed CervicalCareXG, a novelistic system for cervical type cancer diagnostics depends on the XGBoost methodology. CervicalCareXG will utilizes a machine Learning-Driven approach to analyze patient data, also including demographic information, medical history, and cervical screening results, to Prediction the likelihood of cervical type cancer development. The XGBoost algorithm, known for its prediction accuracy and efficacy in handling complex health related datasets, is employed to train a predictive model with a capable of identifying high-risk individuals who may requires further diagnostic evaluation or preventive interventions. Through comprehensively evaluation using reality patient data, CervicalCareXG may demonstrates a promising performance in terms of sensitivity, specificity, and area under the receivers operating characteristic curve (AUC-ROC). Thus, it showcases its potential as a valuable tool for healthcare professionals on cervical type cancer screening programs. By harnessing the power of machine Learning-Driven and XGBoost, CervicalCareXG with an aim to contribute to the early detection and management of cervical type cancer, ultimately by improving patient outcomes and reducing the burden of this devastating disease. © 2024 IEEE.

关键词： Diseases

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Improved Performance of Canny Edge Detection in Low-Light Conditions 3

Improved Performance of Canny Edge Detection in Low-Light Co...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Dadi, Pradeep Surya Saranya, G. Tamilvizhi, T. Nelson, Leema Surendran, R. Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Amrita Vishwa Vidyaapeetham Chennai Campus Amrita School of Computing Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462167

Visual simultaneous localization and mapping (SLAM), which has numerous potential uses, is swiftly emerging as a significant development in embedded vision. In our analysis, there remain several unsolved dilemmas about SLAM that are visual, making it less favored over Lidar SLAM. Because it offers an affordable solution for mapping and navigation in mobile robots. These mobile robots can be defined as robots whose unique base is not fixed, so as technology establishes and advances, we. In this report, a real-time light, that is, dim or image or video in low light problem enhancement algorithm is proposed that significantly improves the overall performance of the Canny Edge part detection algorithm, which usually may be useful for benefit recognition in low light issues in cellular robots for several mapping and navigation tasks. © 2023 IEEE.

关键词： Mobile robots

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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computer Systems science & engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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Efficient Vehicle Detection and Classification using YOLO v8 for Real-Time Applications

Efficient Vehicle Detection and Classification using YOLO v8...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Ravichandran, M. Laxmikant, Kumar Muthu, Annamalai Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India University of Technology & Applied Science Department of Electrical and Electronics Engineering Nizwa Oman

ISBN: (纸本)9798350308167

In the realm of real-time applications such as autonomous driving and surveillance, efficient vehicle detection stands as a paramount concern. The revolutionary You Only Look Once (YOLO) framework, renowned for its rapid object recognition, has reshaped this landscape. This study delves into the fusion of real-time vehicle detection and YOLO v8, optimizing its architecture for swift and precise identification. Empirical validation underscores the model's prowess, achieving a remarkable accuracy of 97.9% on mAP50 and 91.3% on mAP50-95. This achievement not only attests to the model's accuracy but also reaffirms its real-time processing capabilities. The implications resonate across transportation safety and urban planning, promising transformative potential. This research underscores the power of YOLO v8 in intelligent object detection, poised to reshape and enhance various domains. © 2023 IEEE.

关键词： mAP50 mAP50-95 Object Detection Vehicle Detection YOLO Framework

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POSTER: Learning to Parallelize in a Shared-Memory Environment with Transformers 28

POSTER: Learning to Parallelize in a Shared-Memory Environme...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Harel, Re'Em Pinter, Yuval Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Department of Physics Scientific Computing Center Nuclear Research Center Negev Israel Department of Computer Science Ben-Gurion University of the Negev Israel Department of Computer Science Technion - Israel Institute of Technology Scientific Computing Center Nuclear Research Center Negev Israel

ISBN: (纸本)9798400700156

In past years, the world has switched to multi and many core shared memory architectures. As a result, there is a growing need to utilize these architectures by introducing shared memory parallelization schemes, such as OpenMP, to applications. Nevertheless, introducing OpenMP work-sharing loop construct into code, especially legacy code, is challenging due to pervasive pitfalls in management of parallel shared memory. To facilitate the performance of this task, many source-to-source (S2S) compilers have been created over the years, tasked with inserting OpenMP directives into code automatically. In addition to having limited robustness to their input format, these compilers still do not achieve satisfactory coverage and precision in locating parallelizable code and generating appropriate directives. In this work, we propose leveraging recent advances in machine learning techniques, specifically in natural language processing (NLP), to suggest the need for an OpenMP work-sharing loop directive and data-sharing attributes clauses - the building blocks of concurrent programming. We train several transformer models, named PragFormer, for these tasks and show that they outperform statistically-trained baselines and automatic source-to-source (S2S) parallelization compilers in both classifying the overall need for an parallel for directive and the introduction of private and reduction clauses. In the future, our corpus can be used for additional tasks, up to generating entire OpenMP directives. The source code and database for our project can be accessed on GitHub 1 and HuggingFace 2. © 2023 Owner/Author.

关键词： Machine learning

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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