[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124558-en":3,"doc-seo-124558-105":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},124558,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Machine Learning Based Intrusion Detection System - An Experimental Comparison","Machine Learning Based Intrusion Detection System - An Experimental Comparison presents an ML- and DL-driven approach to detecting malicious network traffic in an environment characterized by increasing automation and massive data production. The IDS inspects packet content and classifies traffic as anomalous or normal, using efficient feature selection. A hybrid feature selection method combining Pearson correlation and Random Forest is proposed, followed by training and testing tree-based ML models (Decision Tree, AdaBoost, KNN) and deep learning models (MLP, LSTM) on the TON_IOT dataset. Performance is assessed with accuracy, precision, and recall, concluding strong intrusion detection effectiveness and reduced false rates.","Machine learning based intrusion detection system: an experimental comparison  \nHidayat, Imran; Ali, Muhammad Zulfiqar; Arshad, Arshad  \nPublished in:  \nJournal of Computational and Cognitive Engineering  \nPublication date: 2022  \nDocument Version  \nAuthor accepted manuscript  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nHidayat, I, Ali, MZ & Arshad, A 2022, 'Machine learning based intrusion detection system: an experimental comparison', Journal of Computational and Cognitive Engineering.  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please view our takedown policy at [https://edshare.gcu.ac.uk/id/eprint/5179 for details](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[ ](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[of how to contact us.](of how to contact us.)  \nDownload date: 22. Jul. 2022  \nMachine Learning Based Intrusion Detection System: An Experimental Comparison  \nImran Hidayat 1, Muhammad Zulfiqar Ali 2 and Arshad 2,*  \n1 School of Computing Edinburgh Napier University, UK.  \n2 James Watt School of Engineering, University of Glasgow, UK.  \n3 School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK  \nEmails: [40457769@live.napier.ac.uk](40457769@live.napier.ac.uk); [muhammad.ali@glasgow.ac.uk](muhammad.ali@glasgow.ac.uk)  \nAbstract: Recently, networks are moving towards automation and getting more and more intelligent. With the advent of big data and cloud computing technologies, lots and lots of data is being produced on the internet. Every day petabytes of data are produced from websites, social media sites, or the internet. As more and more data are produced, there is a continuous threat of network attacks also growing. An intrusion Detection System(IDS) is used to detect such types of attacks in the network. IDS inspects packet headers and data and decides whether the traffic is anomalous or normal based on the contents of the packet. In this research, ML techniques are being used for intrusion detection purposes. Feature selection is also used for efficient and optimal featureselection. The research proposes a hybrid feature selection technique composed of the Pearson Correlation Coefficient and Random Forest Model. For the Machine Learning Decision tree, AdaBoost and KNN are trained and tested on the TON_IOT Dataset. The Dataset is new and contains new and recent attack types and features. For Deep Learning (DL), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are trained and tested. Evaluation is done on the basis of accuracy, precision, and recall. It is concluded from the results that the Decision tree for ML and MLP for DL provides optimal accuracy with fewer false positive and negative rates. It is also concluded from the results that the ML techniques are effective for detecting intrusion in the networks.  \nKeywords: MLP, LSTM, KNN, IDS, machine learning.  \n1. Introduction  \nA network intrusion detection system is used to detect unwanted or malicious traffic in the network. An intrusion detection system detects anomalies or attacks in real-time. Now a days mostly applications are moving to the cloud. Due to rapid and fast growth of network devices, security risks got increased. For that reason, the security of cloud infrastructure and network resources is the main priority in the modern world. Therefore, IDS should be accurate, error-free and efficient. Due to the advent of cloud computing, the Internet of Things (IoT) and quantum computing huge amount of data is being created every day, known as big data. This big data also helps in training the machine learning model for securi","cbCaivi6fRda32Hf","https://ap.wps.com/l/cbCaivi6fRda32Hf","pdf",825374,1,23,"English","en",105,"# Introduction\n## IDS types and working principles\n## Related work in intrusion detection\n## Problem statement and paper contributions","[{\"question\":\"What problem does this research address in intrusion detection?\",\"answer\":\"It targets improving intrusion detection accuracy while reducing high false-positive rates and addressing feature selection challenges in IDS.\"},{\"question\":\"How are machine learning and deep learning models used?\",\"answer\":\"The study trains and tests ML models (Decision Tree, AdaBoost, KNN) and DL models (MLP, LSTM) to classify network traffic as anomalous or normal.\"},{\"question\":\"What evaluation metrics are used to compare the approaches?\",\"answer\":\"The research evaluates performance using accuracy, precision, and recall, and reports that Decision Tree (ML) and MLP (DL) achieve optimal accuracy with fewer false positive/negative rates.\"}]","Machine Learning Based Intrusion Detection System - An Experimental Comparison | PDF",1785892987,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-intrusion-detection-system-an-experimental-comparison","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-intrusion-detection-system-an-experimental-comparison/124558/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this research address in intrusion detection?","Question",{"text":75,"@type":76},"It targets improving intrusion detection accuracy while reducing high false-positive rates and addressing feature selection challenges in IDS.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are machine learning and deep learning models used?",{"text":80,"@type":76},"The study trains and tests ML models (Decision Tree, AdaBoost, KNN) and DL models (MLP, LSTM) to classify network traffic as anomalous or normal.",{"name":82,"@type":73,"acceptedAnswer":83},"What evaluation metrics are used to compare the approaches?",{"text":84,"@type":76},"The research evaluates performance using accuracy, precision, and recall, and reports that Decision Tree (ML) and MLP (DL) achieve optimal accuracy with fewer false positive/negative rates.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]