[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125543-en":3,"doc-seo-125543-105":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":4,"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},125543,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Network intrusion detection system using an optimized machine learning algorithm","The rapid expansion of data-communications networks for real-world commercial use increases the need for security and robustness against prominent network intrusions. Network Intrusion Detection Systems (NIDS) address this challenge, while intrusion variants remain widely documented in prior work. Focusing on the Kitsune dataset, the study addresses the gap in comprehensive parametric evaluation using machine learning to determine the best algorithm for detection and classification across eight attack types.","[https://doi.org/10.22581/muet1982.2301.14](https://doi.org/10.22581/muet1982.2301.14)  \n2023, 42(1) 153-164  \nNetwork intrusion detection system using an optimized machine learning algorithm  \nAbdulatifAlabdulatifa, Syed Sajjad Hussain Rizvi b,*  \na Computer Department, College of Science andArts in Ar Rass, Qassim University, Ar Rass Saudi Arabia b Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Karachi Pakistan  \n* Corresponding author: Syed Sajjad Hussain Rizvi Email: [dr.sajjad@szabist.edu.pk](dr.sajjad@szabist.edu.pk)  \nReceived: 19 September 2022, Accepted: 15 December 2022, Published: 01 January 2023  \n\n| KEYWORDS ABSTRACT |  |\n| --- | --- |\n| Network Intrusion Detection Machine Learning\u003Cbr>Hyper-Parameter Optimization Kitsune\u003Cbr>Cyber Security\u003Cbr>Communication Network | The rapid growth of the data-communications network for real-world commercial applications requires security and robustness. Network intrusion is one of the most prominent network attacks. Moreover, the variants of network intrusion have also been extensively reported in the literature. Network Intrusion Detection Systems (NIDS) have already been devised and proposed in the literature to handle this issue. In the recent literature, Kitsune, NIDS, and its dataset have received approx.\u003Cbr>500 citations so far in 2019. But, still, the comprehensive parametric evaluation of this dataset using a machine learning algorithm was missing in the literature that could submit the best algorithm for network intrusion attack detection and classification in Kitsune. In this connection, two previous studies were reported to investigate the best machine algorithm (these two studies were reported by us) . Through these studies, it was concluded that the Tree algorithm and its variants are best suited to detect and classify all eight types of network attacks available in the Kitsune dataset. In this study, the hyper-parameter optimization of the optimized Tree algorithm is presented for all eight types of network attack. In this study, the optimizer functions Bayesian, Grid Search, and Random Search were chosen. The performance has been ranked based on training and testing accuracy, training and testing cost, and prediction speed for each optimizer. This study will submit the best point hyper-parameter for the respective epoch against each optimizer. |\n\n1. Introduction  \nSince the last decade, the NIDS has gained major popularity in the domain of cyber security [1] . It is because of a multi-folded reason which includes, but isnot limited to, integration of business applications with communication networks; global integration of businesses; online businesses and ventures; remote working; etc. [2] . The performance, efficiency, and robustness of the classical NIDS were significantly uplifted by integrating the NIDS with artificial  \nintelligence [3] . Although in the recent literature, the researchers have proposed various Deep Learning and Machine Learning based solutions to make NIDS effective and competent in identifying malevolent attacks. However, the rapid increase in the network traffic and the emerging safety threats has posed by challenges for NIDS system for the detection of malevolent intrusions efficiently. In recent literature, significant work has been reported in the common domain of NIDS and artificial intelligence.  \nMulti-stage optimization of machine learning algorithm for NIDS [4], BAT modeling for NIDS [5], Boosting algorithm for NIDS, 1D CNN based NIDS [6], stacking ensemble for NIDS [7], ensemble-based NIDS [8] etc. However, all these existing methods are found tobe deficient for optimal machine learning perimeters. Whereas the proposed algorithm is trained for real time NIDS and have been tuned for optimal machine learning perimeter. For robust and intelligent NIDS, machine learning and deep learning-based algorithms require well-structured and domain-oriented datasets. In this connection, many researchers and research groups have ","cbCaifyZZbWgu3kC","https://ap.wps.com/l/cbCaifyZZbWgu3kC","pdf",911202,1,12,"English","en",105,"# Introduction\n## Role of NIDS and AI\n## Datasets for NIDS with focus on Kitsune\n## Research gap and study objective","[{\"question\":\"What problem does the study address in network intrusion detection?\",\"answer\":\"It targets the lack of comprehensive parametric evaluation of the Kitsune dataset with machine learning methods to identify the best approach for intrusion detection and classification.\"},{\"question\":\"Which dataset and how many attack types are considered?\",\"answer\":\"The study focuses on the Kitsune dataset and evaluates performance across eight types of network attacks.\"},{\"question\":\"How is the optimized tree algorithm tuned and evaluated?\",\"answer\":\"Hyper-parameter optimization is performed using Bayesian, Grid Search, and Random Search, and performance is ranked by training/testing accuracy, training/testing cost, and prediction speed.\"}]","Network intrusion detection system using an optimized machine learning algorithm | 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problem does the study address in network intrusion detection?","Question",{"text":75,"@type":76},"It targets the lack of comprehensive parametric evaluation of the Kitsune dataset with machine learning methods to identify the best approach for intrusion detection and classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and how many attack types are considered?",{"text":80,"@type":76},"The study focuses on the Kitsune dataset and evaluates performance across eight types of network attacks.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the optimized tree algorithm tuned and evaluated?",{"text":84,"@type":76},"Hyper-parameter optimization is performed using Bayesian, Grid Search, and Random Search, and performance is ranked by training/testing accuracy, training/testing cost, and prediction 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