[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126771-en":3,"doc-seo-126771-105":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},126771,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Weighted Feature Selection for Machine Learning Based Accurate Intrusion Detection in Communication Networks - Research and Results","Network intrusion detection systems face degraded detection quality and increased training cost due to noisy data and large, redundant feature sets. This work proposes a weighted feature selection strategy that emphasizes features with high impact on the target variable to improve intrusion attack detection. Experiments use the CICIDS-2017 dataset. The approach reduces irrelevant features by about 51%, while raising the tuned random forest classifier accuracy to 99.9% with nearly 50% lower model computation time.","Received 11 January 2024, accepted 30 January 2024, date of publication 6 February 2024, date of current version 13 February 2024. Digital Object Identifier 10.1109/ACCESS.2024.3362794  \nWeighted Feature Selection for Machine Learning Based Accurate Intrusion Detection in Communication Networks  \nGAURAV TRIPATHI1, VISHAL KRISHNA SINGH2, VARUN SHARMA1, AND MAJITHIA VIVEK VINODBHAI3  \n1Department of Computer Science, Indian Institute of Information Technology, Lucknow, Uttar Pradesh 226002, India  \n2 School of Computer Science and Electronics Engineering, University of Essex, CO4 3SQ Colchester, U.K.  \n3TCS Innovation Laboratories, Infocity, Gandhinagar, Gujarat 382421, India Corresponding author: Vishal Krishna Singh ([v.k.singh@essex.ac.uk](v.k.singh@essex.ac.uk))  \nABSTRACT Network intrusion detection systems work on huge data sets, with large feature sets dominated by noisy data and irrelevant features, resulting in steep degradation in detection accuracy and a steep proliferation in model training and computation time. This work presents a novel method to optimize the feature selection process in machine learning algorithms for accurate detection of intrusion attacks in communication networks. The proposed method targets features with a high impact on the target variable to optimize feature selection and reduction. The CICIDS-2017 data set is used to test the performance of the proposed approach. Results prove the dexterity of the proposed method as it is able to achieve an almost 51% reduction in irrelevant features and increases the detection accuracy of the tuned random forest classifier to 99.9% with an almost 50% reduced model computation time.  \nINDEX TERMS Communication networks, machine learning, random forest, intrusion detection, network attacks.  \nI. INTRODUCTION  \nIntrusion Detection System (IDS) is an effective technique for prevention against cyber-attacks in communication networksand for preventing intrusion in communication systems [1] . Emerging technologies such as Big Data, Internet of Things (IoT), Edge Computing, Cloud Computing, Wirleless Sensor Networks (WSNs), etc., [2], [3], [4], [5], [6], [7], [8] generate a large amount of multidimensional data with numerous features that must be reduced to create an efficient IDS. Consequently, optimization techniques are applied to these heterogeneous massive data sets, not only because of the increasing number of tuples but also because of the exceedingly high number of irrelevant features in each tuple, which may result in high false positives, redundant observations, and high computational complexity.  \nAn important aspect of the degrading performance of existing IDS has been poor feature extraction, which is  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Ghufran Ahmed .  \na major focus of the proposed work. Feature selection is imperative to reduce the non-contributing features in the classification, which have a significant impact on the efficiency and accuracy of the IDS. Therefore, if the data set is divided into components that are less redundant and have more accurate logs, the testing component will have better model performance. As a result, for optimal IDS, choosing a less redundant data set for testing and training components of the system model is one of the most important steps to improve the accuracy and performance of the communication networks.  \nNotably, recent research has proved that the methods of machine learning (ML) can considerably enhance the performance of network IDS by making them less complex, more proactive, and less costly, by reducing the time spent on repeated tasks, and by allowing businesses to employ their resources more efficiently, given that they are supplemented by data that completely represents the environment. ML approaches have been widely used to successfully detect network intrusions or abnormal behavior  \n􀀊 2024 The Authors. This work is licensed under a Crea","cbCaivfZIk3afejw","https://ap.wps.com/l/cbCaivfZIk3afejw","pdf",2378521,1,10,"English","en",105,"# Introduction\n## Intrusion Detection and Feature Reduction Need\n## Prior Machine Learning Approaches for IDS\n## Motivation and Focus of the Proposed Work","[{\"question\":\"Why does intrusion detection accuracy degrade in communication networks using machine learning?\",\"answer\":\"Large feature sets often contain noisy and irrelevant features, which increase false positives and computational complexity, leading to lower detection accuracy and higher training time.\"},{\"question\":\"What is the core idea of the proposed weighted feature selection method?\",\"answer\":\"It targets features with high impact on the target variable to optimize feature selection and reduction for more accurate intrusion attack detection.\"},{\"question\":\"What dataset and performance improvements are reported?\",\"answer\":\"The method is tested on CICIDS-2017, achieving about 51% reduction in irrelevant features and increasing tuned random forest detection accuracy to 99.9%, with nearly 50% reduced model computation time.\"}]","Weighted Feature Selection for Machine Learning Based Accurate Intrusion Detection in Communication Networks - 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