[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122502-en":3,"doc-seo-122502-105":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},122502,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Performance Evaluation of Machine Learning Algorithms for Intrusion Detection - Thesis","In the cyber security domain, increasing attack sophistication drives the need for more reliable detection. This research evaluates machine learning approaches—Decision Tree, Support Vector Machine (SVM), Random Forest, and Naive Bayes—for intrusion detection using the KDD Cup 99 dataset and Python for validation. Model performance is assessed through accuracy, precision, recall, and F1 score, while additional analysis considers the influence of data sets and feature counts on classification effectiveness. Results show SVM and Random Forest outperform Decision Tree and Naive Bayes, with Random Forest achieving the strongest overall intrusion classification performance.","PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS FOR  \nINTRUSION DETECTION  \nby  \nNSOVO MILDRED NDLEVE  \nDISSERTATION / THESIS  \nSubmitted in fulfilment of the requirements for the degree of  \nMASTER OF SCIENCE  \nin  \nCOMPUTER SCIENCE  \nin the  \nFACULTY OF SCIENCE & AGRICULTURE School of Mathematical and Computer Science  \nat the  \nUNIVERSITY OF LIMPOPO  \nSupervisor: Professor SN MOKWENA  \n2024  \nDECLARATION  \nI, NSOVO MILDRED NDLEVE, solemnly declare that the dissertation entitled'Performance Evaluation Of Machine Learning Algorithms For Intrusion Detection'represents the culmination of my individual research endeavours. I declare that all sources incorporated or cited in this document are duly acknowledged by meticulous referencing. Furthermore, I affirm that this dissertation has not been previously presented for the attainment of any other academic degree at any other institution. This dissertation is hereby submitted to the University of Limpopo in fulfilment of the requirements for the degree of Master of Science in Computer Science.  \nNDLEVE NM  Surname, Initials (Ms)  \n2023/12/01   \nDate  \nACKNOWLEDGMENTS  \nI would like to extend my heartfelt appreciation to my supervisor, Prof. NS Mokwena, for his unwavering support throughout my academic journey. His patience, inspiration, passion, and extensive knowledge have been invaluable to me. I am grateful for his guidance and mentorship, which greatly contributed to the success of my study. Having him as an advisor has exceeded my expectations.  \nMy sincere gratitude extends to my late father, Tinyiko Ndleve, and my mother, Tsakani Ndleve, for their enduring love, prayers, care, and sacrifices. Their unwavering support has been a source of strength, shaping me into the person I am today. I would also like to acknowledge the presence and influence of my siblings, Mandla, Nyeleti, Mixo, and Ntsakelo, whose encouragement and camaraderie have been a constant throughout my academic journey.  \nI extend my appreciation to my extended family, friends and colleagues who have been a pillar of support, providing encouragement and understanding during challenging times. Each of you has played a crucial role in my academic success and I am truly grateful for your presence in my life.  \nLastly, I express my gratitude to the Almighty for guiding me through the challengesand triumphs of my academic pursuits. His grace has been my constant companion, and I acknowledge His role in enabling me to complete my degree. I place my faith in Him for the journey ahead. Thank you, Lord.  \nABSTRACT  \nIn the cyber security domain, the increasing sophistication of attacks requires the development of improved detection techniques. This research looked at the usefulness of machine learning methods, especially Decision Tree, Support Vector Machine (SVM), Random Forest, and Naive Bayes, in intrusion detection. The aim of the study was to use these techniques to train data sets using the KDD Cup 99 data set and the Python programming language for validation. Performance evaluations were carried out to examine accuracy, precision, recall, and F1 score, giving insight on the strengths and drawbacks of each algorithm.  \nThe investigation also examined the impact of Decision Tree, SVM, Random Forest, and Naive Bayes on intrusion detection, taking into consideration data sets and feature counts to assess the effectiveness of each model. The study addressed pertinent aspects, including the comparative performance of different algorithms, their suitability for diverse types of intrusions, and the factors that influence their efficacy.  \nTraditional intrusion detection methods frequently fail to detect modern attacks, resulting in high false-positive and false negative rates. Machine learning algorithms, on the other hand, took a more dynamic approach, and this study aimed to elucidate their performance characteristics. This study contributed to the evolving landscape of intrusion detection by delving into the co","cbCaibJdmxvJMFnw","https://ap.wps.com/l/cbCaibJdmxvJMFnw","pdf",1162029,1,93,"English","en",105,"# Declaration\n# Acknowledgments\n# Abstract\n# Table of Contents\n# List of Tables\n# List of Figures\n# Chapter 1: Introduction\n## Background of the Study\n## Problem Statement\n## Motivation\n## Methodology and Analytical Procedures\n## Data Analysis\n## Scientific Contribution\n## Availability of Resources\n## Ethical Consideration\n# Chapter 2: Literature Review\n## Introduction\n## Overview of the Intrusion Detection System (IDS)\n## Machine Learning Algorithms for Intrusion Detection\n## Performance Evaluation Studies\n## Summary of Related Work and Identified Gaps\n## Conclusions\n# Chapter 3: Methodology","[{\"question\":\"Which machine learning algorithms are evaluated for intrusion detection?\",\"answer\":\"The study evaluates Decision Tree, Support Vector Machine (SVM), Random Forest, and Naive Bayes.\"},{\"question\":\"What dataset and validation tools are used in the research?\",\"answer\":\"Experiments use the KDD Cup 99 dataset, with Python employed for validation.\"},{\"question\":\"How are the algorithms’ performances measured?\",\"answer\":\"Performance is measured using accuracy, precision, recall, and F1 score, with additional consideration of data sets and feature counts.\"}]","Performance Evaluation of Machine Learning Algorithms for Intrusion Detection - 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