[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119688-en":3,"doc-seo-119688-105":30,"detail-sidebar-cat-0-en-105":83},{"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},119688,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","A Dependable Hybrid Machine Learning Model for Network Intrusion Detection - Abstract and Introduction","Network intrusion detection systems (NIDSs) are essential to computer network security, yet anomaly-based automated methods face major challenges as attacks grow in sophistication and the data volume required for training increases. This research presents a hybrid machine-learning and deep-learning model designed to improve detection performance while ensuring dependability. Efficient preprocessing combines SMOTE for data balancing with XGBoost for feature selection, then selects the best benchmarked model for pipeline deployment. Experiments on KDDCUP'99 and CIC-MalMem-2022 report 99.99% and 100% accuracy without overfitting or Type-1/Type-2 issues.","arXiv :2212 .04546v2 [ cs .CR] 27 Jan 2023  \nA Dependable Hybrid Machine Learning Model for Network Intrusion Detection  \nMd. Alamin Talukdera , Khondokar Fida Hasanb , Md. Manowarul Islama , Md Ashraf Uddina , Arnisha Akhtera ,  \nMohammad Abu Yousufc , Fares Alharbid , Mohammad Ali Monie  \na Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh b Information Security Discipline, School of Computer Science, Queensland University of Technology (QUT), 2 George Street,  \nBrisbane 4000, Australia  \nc Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh  \nd Computer Science Department, Shaqra University, Shaqra 15526, Saudi Arabia  \neArti􀀌cial Intelligence & Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences,  \nThe University of Queensland St Lucia, QLD 4072, Australia.  \nAbstract  \nNetwork intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others signi􀀌cantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issue in the development of anomaly-based NIDS. However, do current models meet the needs of today's networks in terms of required accuracy and dependability? In this research, we propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our proposed method ensurese􀀎cient pre-processing by combining SMOTE for data balancing and XGBoost for feature selection. We compared our developed method to various machine learning and deep learning algorithms in order to 􀀌nd a more e􀀎cient algorithm to implement in the pipeline. Furthermore, we chose the most e􀀋ective model for network intrusion based on a set of benchmarked performance analysis criteria. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022, with an accuracy of 99.99% and 100% for KDDCUP'99 and CIC-MalMem-2022, respectively, and no over􀀌tting or Type-1 and Type-2 issues.  \nKeywords: Intrusion Detection System, Machine Learning, XGBoost, Feature Selection, Feature Importance, Accuracy, Dependability  \n1. Introduction  \nInternet-based computer networks are becoming more vulnerable to security threats. The constant emergence of new types of threats makes developing dependable and adaptable security strategies a critical issue. Important data is always a target for attackers, making it vulnerable to concentrated network attacks. The process by which an attacker gains access to a system or system server and then sends malicious packets to the user system in order to steal, modify, or corrupt any sensitive or vital data is referred to as intrusion. An attack is de􀀌ned as the unauthorized transmission of network packets or malicious conduct over a network. Existing system vulnerabilities,  \n􀀃 Mohammad Ali Moni  \n􀀃􀀃 Md Manowarul Islam  \nEmail addresses: [mdalamintalukdercsejnu@gmail.com](mdalamintalukdercsejnu@gmail.com) (Md. Alamin Talukder), [fida.hasan@qut.edu.au](fida.hasan@qut.edu.au) (Khondokar Fida  \nHasan), [manowar@cse.jnu.ac.bd](manowar@cse.jnu.ac.bd) (Md. Manowarul Islam), [ashraf@cse.jnu.ac.bd](ashraf@cse.jnu.ac.bd) (Md Ashraf Uddin), [arnisha@cse.jnu.ac.bd](arnisha@cse.jnu.ac.bd)  \n(Arnisha Akhter), [yousuf@juniv.edu](yousuf@juniv.edu) (Mohammad Abu Yousuf), [faalhrbi@su.edu.sa](faalhrbi@su.edu.sa) (Fares Alharbi), [m.moni@uq.edu.au](m.moni@uq.edu.au)  \n(Mohammad Ali Moni)  \nsuch as user error, miscon􀀌guration, or software 􀀍aws, may allow the intrusion to occur on the server or system. An intelligent intrusion can also be carried out by combining various system vulnerabilities.  \nIn a global network, a vast number of online services and millions of massive servers are active. As a result, as these networks become more appealing to attackers, they re","cbCaiu0UKpFonEE5","https://ap.wps.com/l/cbCaiu0UKpFonEE5","pdf",1698904,1,44,"English","en",105,"# Abstract\n# 1. 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