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A review on machine learning techniques for network security

Journal of Cyber Security Technology 2025年

作者： Nazir, Rashid Laghari, Asif Ali Dahri, Fida Hussain Shoulin, Yin Alhakeem, Zaineb M. Hakim, Heba Mughal, Zeeshan Ahmed Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan Software college Shenyang Normal University Shenyang China School of Computer Science and Engineering Southeast University Nanjing China Chemical Engineering and Oil Refining Department Oil and Gas Engineering College Basrah University for Oil and Gas Basrah Iraq Computer Engineering Department Engineering College University of Basrah Basrah Iraq

Network security encompasses the strategies and techniques to protect networks from unauthorized access and potential threats. Network security is essential to protect layers of networks and data transferring on them. Data integrity and the underlying network infrastructure are necessary in today’s digital landscape. This paper explores the role of machine learning (ML) in enhancing network security by developing intelligent tools and methodologies. We critically analyze various ML techniques utilized in this field, their practical applications, and the advancements achieved in fortifying networks against cyber threats. We address ML’s challenges and limitations in network security, concluding with a discussion of open research issues for future investigation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Network intrusion

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Real-Time Facial Recognition System for Secure College Bus Transport using Deep Learning Techniques

Real-Time Facial Recognition System for Secure College Bus T...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M. N. Kavitha Saranya S S Pragatheeswari E N. Kaviya Priya S R. Ranjith N. Rahul Vanchivel Department of Computer Science and Design Kongu Engineering College Tamil Nadu India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore Tamil Nadu India Department of Computer Science and Engineering Kongu Engineering College Erode Tamil Nadu India Department of CSD UG Student Kongu Engineering College Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability.

关键词： Support vector machines Deep learning Renewable energy sources Accuracy Face recognition Transportation Nearest neighbor methods Real-time systems Security Face detection

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Enhancing Retrieval Augmented Generation Systems Using AI Models and Graph Databases

Enhancing Retrieval Augmented Generation Systems Using AI Mo...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Souvik Mukherjee Soosan Shabnam Sara Hasan D. Ajitha Department of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

This paper outlines the process of generating a Neo4j graph database powered by Language Models (LLMs). The primary goal is to extract structured information from unstructured data, including user profiles, paper briefs, and Slack messages, and convert them into Cypher queries. The data is then ingested into Neo4j to build a graph database that captures relationships between users, paper, technologies, and messages. A pipeline was developed to automate the process, ensuring accurate entity and relationship extraction using predefined templates. This approach allows for efficient data representation and supports consultancy in managing large datasets by generating insightful visualizations and querying capabilities.

关键词： Ciphers Accuracy Databases Pipelines Retrieval augmented generation Loading Data visualization Transforms Paper technology Data mining

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Segmenting the Brain Tumor using Deep Learning Techniques

Segmenting the Brain Tumor using Deep Learning Techniques

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Ch. Lakshmi Pranitha R. Sumathi V.Harshitha N.Kavya A.Soni Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study presents a U-Net architecture with EfficientNet-B7 as the feature extraction backbone is used to propose a robust brain tumor segmentation method. Using cutting-edge deep learning techniques, the suggested model precisely segments MRI images with an emphasis on identifying and defining tumor anomalies. A thorough preprocessing pipeline that handles missing values, augments data with albumentations, and normalizes data guarantees the consistency and resilience of the incoming data. The segmentation procedure uses a hybrid loss function that combines Soft Dice Loss and Binary Cross-Entropy (BCE), improving the model's ability to strike a compromise between segmentation consistency and pixel-level precision. To ensure efficiency and generalizability, the model training is optimized using the Adam optimizer in conjunction with a learning rate scheduler. Additionally, an early halting mechanism is incorporated to minimize overfitting. Using a standardized dataset, performance evaluation is carried out with 70%, 15%, and 15% splits for training, validation, and testing, respectively. To evaluate the quality of segmentation, important metrics are employed, such as the Dice Coefficient and the Intersection over Union (IoU). The accuracy of the model in identifying tumor boundaries is illustrated by visualizations of the anticipated segmentation results. The potential of combining U-Net with cutting-edge feature extractors such as EfficientNet-B7 to produce dependable, effective, and clinically significant segmentation results in medical imaging is demonstrated by this approach.

关键词： Training Deep learning Image segmentation Renewable energy sources Magnetic resonance imaging Brain tumors Feature extraction Brain modeling Testing Resilience

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Crypto Trading Platform: MERN Stack with Real-Time Data and Social Features

Crypto Trading Platform: MERN Stack with Real-Time Data and ...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Gurupriya M Mouhitha A Saranya Gujjula Okesh Reddy Ankireddypalli Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The increasing complexity of cryptocurrency markets necessitates the development of efficient portfolio management tools that provide real-time tracking, price updates, and market awareness. This paper focuses on an advanced concept of a cryptocurrency wallet service provision that provides a summary of available coins, their prices, available quantities, and customers‘ transaction histories. Developed for the ‘frontend’ using *** and for the ‘backend’ using ***, which uses the CoinGecko API for receiving live market prices and collecting curated news blog articles from other APIs. The data related to the wallets are efficiently managed since the application is built based on the MongoDB schema, and in addition to this, the user authentication is also integrated. A responsive design of the user interface allows for showing both a portfolio summary and the latest news related to cryptocurrencies, thus suggesting an ‘everything in one place’ solution for users interested in cryptocurrencies. This system shows how real-time data integration could be implemented, how the system is friendly to users, and how the system manages the content. This system could be expanded in the future with predictive analytics and an AI recommendation system for the portfolio management and contents.

关键词： Online banking Scalability Blogs Virtual environments User interfaces Real-time systems Cryptocurrency Security Portfolios Testing

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural Computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid

arXiv

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arXiv 2025年

作者： Dash, Smruti P. Khandeparkar, Kedar V. Agrawal, Nipun Department of Computer Science and Engineering Indian Institute of Technology Dharwad Karnataka Dharwad580011 India Department of Computer Science and Engineering Rajiv Gandhi Institute of Technology Karnataka Bangalore560032 India

The modern power grids are integrated with digital technologies and automation systems. The inclusion of digital technologies has made the smart grids vulnerable to cyber-attacks. Cyberattacks on smart grids can compromise data integrity and jeopardize the reliability of the power supply. Traditional intrusion detection systems often need help to effectively detect novel and sophisticated attacks due to their reliance on labeled training data, which may only encompass part of the spectrum of potential threats. This work proposes a semi-supervised method for cyber-attack detection in smart grids by leveraging the labeled and unlabeled measurement data. We implement consistency regularization and pseudo-labeling to identify deviations from expected behavior and predict the attack classes. We use a curriculum learning approach to improve pseudo-labeling performance, capturing the model uncertainty. We demonstrate the efficiency of the proposed method in detecting different types of cyberattacks, minimizing the false positives by implementing them on publicly available datasets. The method proposes a promising solution by improving the detection accuracy to 99% in the presence of unknown samples and significantly reducing false *** Codes 68T07 (Primary), 68T27, 68T37 (Secondary) © 2025, CC BY.

关键词： Cyber attacks

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Exploring the Performance of Meta Learning Strategies for Medical Image Segmentation with Transfer Learning 5th

Exploring the Performance of Meta Learning Strategies for Me...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Yadav, Dhyan Chandra Pandey, Vijay Kumar Ranjan, Vivek Karthikeyan, M.P. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mechanical Engineering Vivekananda Global University Jaipur India Department of Computer Science Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

This paper explores the overall performance of meta-gaining knowledge of strategies for medical photo segmentation with switch mastering. It discusses the latest advancements in deep gaining knowledge of and switching learning strategies and their software to clinical photo segmentation. Specifically, the examiner evaluates meta-studying strategies, especially for clinical image segmentation, which aim to optimize the parameters of conventional transfer learning strategies. First, the paper opinions the unique varieties of switch learning strategies, which include pre-skilled models, area version, transfer studying inside the convolution neural networks (CNNs), and multi-undertaking getting to know, and affords an outline in their packages. Then, numerous meta-studying strategies are mentioned, including reinforcement mastering, multi-assignment getting to know, version ensembles, and meta-gaining knowledge of algorithms. They have a look at evaluating those meta-getting-to-know strategies on three public clinical photo segmentation datasets: GlaS, okay, and Acropolis. It concludes that the proposed meta-learning strategies yield massive performance improvements compared to traditional transfer learning strategies, particularly for datasets with significant area shifts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Efficient Edge-AI with Binarized Neural Networks and CMOS Image Sensors: A Sparsity-Driven Approach

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Circuits, Systems, and Signal Processing 2025年 1-17页

作者： Kisku, Wilfred Kaur, Amandeep Mishra, Deepak Department of Electrical Engineering Indian Institute of Technology Jodhpur Rajasthan India Department of Computer Science and Engineering Indian Institute of Technology Jodhpur Rajasthan India

With the increasing adoption of Edge AI devices, designing efficient machine learning systems requires optimizing both computational models and sensor architectures. While, Binarized Neural Networks (BNNs) offer a promising solution by significantly reducing memory and computational demands, this work introduces a co-design methodology that integrates sparsity with reduced ADC precision during acquisition at the sensor level to enhance efficiency. The approach involves edge extraction within images in the analog domain, followed by selective quantization level at the ADC, reducing redundant information before digital processing. The impact of varying sparsity levels, defined by [4, 3, 2, 1] conversion bits, is evaluated on BNN models, including AlexNet, VGG, ResNet, SqueezeNet, and MobileNet. Experimental results on CIFAR-10 and STL-10 datasets demonstrate that reducing ADC precision from 4-bit to 1-bit leads to an approximate 10% accuracy drop (from 65.27% to 54.85% in AlexNet on CIFAR-10), while achieving significant reductions in memory and power consumption. Specifically, the proposed approach reduces ADC power by approximately 94%, lowering energy requirements from 15.61 mW to 0.86 mW for a 32×32 pixel array. These results validate the effectiveness of sensor-driven sparsity in enabling energy-efficient AI inference, highlighting its potential for real-world edge computing applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Edge detection

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A Hybrid U-Net with Active Contours for Plant Leaf Disease Segmentation and Classification

A Hybrid U-Net with Active Contours for Plant Leaf Disease S...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Sivamurugan J Naveen G Rajalakshmi D P. Venkata Subba Reddy Shaik Taher Sannapu Deepak Department of Computer Science and Engineering Madha Institute of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Madha Institute of Engineering and Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College Thiruvallur India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The economic growth of a nation entirely depends upon the agriculture and agricultural products. In developing countries like India, agriculture is the primary source of income and its contributing 17% to the total GDP. There are plenty of factors lead to plant disease which impacts the quality and yield of plants. Though manual method of detection is time consuming and it may have chance for errors. This method is not enough to identify and limit the spread of plant disease. To establish an automated plant disease detection in farms will reduce the risk of plant disease and promotes real-time monitoring of crops in a daily basis. Artificial Intelligence (AI) took part in supporting farmers to get instant solution in selection of fertilizers, classifying the quality of agricultural products, weather prediction and soil nutrient level detection. In this study, we proposes a novel segmentation approach namely Hybrid U-Net with active contours to segment the disease affected portion on leaf which support farmers to identify disease at early stage. This study provides a comprehensive analysis of plant disease segmentation by proposed method with conventional approaches. The publically available dataset is chosen for this analysis and performance of conventional studies was compared. This study presented current trends of plant disease segmentation and several other image classification techniques. Experimental results evaluating that the proposed study improved segmentation better than conventional methods.

关键词： Plant diseases Image segmentation Renewable energy sources Accuracy Filtering Neural networks Weather forecasting Soil Artificial intelligence Active contours

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