[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123125-en":3,"doc-seo-123125-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123125,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Advancing Network Security - Comparative Research on Machine Learning Techniques for Intrusion Detection","Advances in network technologies have increased security vulnerabilities, requiring more dynamic defenses against modern threats. This study integrates machine learning into intrusion detection systems to compare algorithms such as k-nearest neighbors, logistic regression, and perceptron neural networks. Experiments use the NSL-KDD dataset with principal component analysis to reduce features to 20 components and evaluate detection of DoS, probe, U2R, and R2L intrusions. Results show neural networks achieve strong accuracy, precision, and recall, supporting reliable IDS deployment.","Advancing network security: a comparative research of machine learning techniques for intrusion detection  \nShynggys Rysbekov1, Abylay Aitbanov1, Zukhra Abdiakhmetova2, Amandyk Kartbayev1  \n1School of Information Technology and Engineering, Kazakh-British Technical University, Almaty, Kazakhstan 2Informatics Department, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Kazakhstan  \n\n| Article history:\u003Cbr>Received Mar 1, 2024 Revised Nov 5, 2024 Accepted Nov 20, 2024 | In the current digital era, the advancement of network-based technologies has brought a surge in security vulnerabilities, necessitating complex and dynamic defense mechanisms. This paper explores the integration of machine learning techniques within intrusion detection systems (IDS) to tackle the intricacies of modern network threats. A detailed comparative analysis of various algorithms, including k-nearest neighbors (KNN), logistic regression, and perceptron neural networks, is conducted to evaluate their efficiency in detecting and classifying different types of network intrusions such as denial of service (DoS), probe, user to root (U2R), and remote to local (R2L) . Utilizing the national software laboratory knowledge discovery and data mining (NSL-KDD) dataset, a standard in the field, the study examines the algorithms’ ability to identify complex patterns and anomalies indicative of security breaches. Principal component analysis is utilized to streamline the dataset into 20 principal components for data processing efficiency. Results indicate that the neural network model is particularly effective, demonstrating exceptional performance metrics across accuracy, precision, and recall in both training and testing phases, affirming its reliability and utility in IDS. The potential for hybrid models combining different machine learning (ML) strategies is also discussed, highlighting a path towards more robust and adaptable IDS solutions.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Anomaly detection Hybrid model\u003Cbr>Intrusion detection systems Machine learning Network security\u003Cbr>Neural networks Oversampling methods |  |\n\nCorresponding Author:  \nAmandyk Kartbayev  \nSchool of Information Technology and Engineering, Kazakh-British Technical University Tole Bi 59, Almaty, Kazakhstan  \nEmail: [a.kartbayev@gmail.com](a.kartbayev@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn recent years, the rapid expansion of internet of things (IoT) technologies have significantly increased both the number of internet users and the variety of applications, consequently heightening network connectivity. This development, however, has introduced numerous security vulnerabilities. Traditional security measures like firewalls, data encryption, and user authentication have been deployed to counteract these threats. Although effective against many types of attacks, these conventional methods often lack the capacity for in-depth packet analysis, leaving them vulnerable to more complex attacks. To address these limitations, intrusion prevention systems (IPS) and intrusion detection systems (IDS) have been implemented. These systems, utilizing sophisticated algorithms from machine learning, deep learning, and artificial intelligence, provide a deeper data analysis to improve attack detection. While IPS units combine detection and prevention functionalities, IDS units focus primarily on detection and traffic analysis.  \nThe surge in internet connectivity and data transfer rates has also led to an increase in network anomalies and a corresponding rise in cyber-attacks. According to a recent vulnerability and threat report by  \nSkybox Security, there were 17 thousand new vulnerabilities recorded in 2019, reflecting a 3.8% increase from the previous year. In response to these growing threats, both the public and private sectors are significantly increasing their investment in cybersecurity technologi","cbCaiubhftxN9kLl","https://ap.wps.com/l/cbCaiubhftxN9kLl","pdf",407524,1,11,"English","en",105,"# Abstract\n# 1. Introduction\n## Background: IoT growth and rising vulnerabilities\n## Limitations of traditional security measures\n## Role of IPS and IDS\n# IDS detection approaches\n## Signature-based IDS\n## Rule-based (anomaly-based) IDS","[{\"question\":\"What problem does the paper address in network security?\",\"answer\":\"It addresses the growing number of network security vulnerabilities and complex modern threats that traditional defenses struggle to handle.\"},{\"question\":\"Which machine learning methods are compared for intrusion detection?\",\"answer\":\"The paper compares k-nearest neighbors, logistic regression, and perceptron neural networks, evaluating their ability to classify intrusion types.\"},{\"question\":\"How does the study prepare data and measure IDS performance?\",\"answer\":\"It uses the NSL-KDD dataset and applies principal component analysis to reduce data to 20 principal components, then evaluates accuracy, precision, and recall on training and testing phases.\"}]","Advancing Network Security - 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