[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123888-en":3,"doc-seo-123888-105":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},123888,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Fortifying network security - Machine Learning-Powered Intrusion Detection Systems and Classifier Performance Analysis","Machine learning methods strengthen intrusion detection systems by improving how network threats are detected and classified. The study focuses on the CICIDS 2017 dataset and refines it through preprocessing steps including feature normalization, class imbalance handling, feature reduction, and feature selection to improve data quality and model robustness. Three classifiers are evaluated: SVM, XGBoost, and naive Bayes. SVM achieves strong overall metrics around 0.984, while XGBoost reaches perfect scores across all measures. Results highlight that careful preparation directly improves IDS effectiveness and supports efficient intrusion detection and prevention.","Fortifying network security: machine learning-powered intrusion detection systems and classifier performance analysis  \nArar Al Tawil1, Lara Al-Shboul2, Laiali Almazaydeh3, Mohammad Alshinwan1,4  \n1Faculty of Information Technology, Applied Science Private University, Amman, Jordan 2King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan 3College of Information Technology, Al-Hussein Bin Talal University, Ma’an, Jordan  \n4MEU Research Unit, Middle East University, Amman, Jordan  \nArticle history:  \nReceived Feb 21, 2024 Revised Jun 18, 2024 Accepted Jul 1, 2024  \nKeywords:  \nClass imbalance handling Classification  \nFeature selection Intrusion detection systems Preprocessing  \nCorresponding Author:  \nIntrusion detection systems (IDS) protect networks from threats; they actively monitor network activity to identify and prevent malicious actions. This study investigates the application of machine learning methods to strengthen IDS, explicitly emphasizing the comprehensive CICIDS 2017 dataset. The dataset was refined by implementing stringent preprocessing methods such as feature normalization, class imbalance management, feature reduction, and feature selection to ensure its quality and lay the foundation for developing robust models. The performance evaluation of three classifiers-support vector machine (SVM), extreme gradient boosting (XGBoost), and naive Bayes was highly impressive. Vital accuracy, precision, recall, and F1-score values of 0.984389, 0.984479, 0.984375, and 0.984304, respectively, were achieved by SVM. Notably, XGBoost demonstrated exceptional performance across all metrics, attaining flawless scores of 1.0. naive Bayes demonstrated noteworthy accuracy, precision, recall, and F1-score performance, which were recorded as 0.877392, 0.907171, 0.877007, and 0.876986, respectively. The results of this study emphasize the critical importance of preparation methods in improving the effectiveness of IDS via machine learning. This further demonstrates the potential of particular classifiers to detect and prevent network intrusions efficiently, thereby substantially contributing to cybersecurity measures.  \nThis is an open access article under the CC BY-SA license.  \nArar Al Tawil  \nFaculty of Information Technology, Applied Science Private University Amman, Jordan  \nEmail: [ar_altawil@asu.edu.jo](ar_altawil@asu.edu.jo)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nAn intrusion detection system (IDS) is a device or application that monitors a network or systems to detect and prevent malicious activity or policy violations are known as an IDS. It is possible to find IDS variants customized to accommodate diverse levels of security, spanning from individual computer systems to vast networks. The primary classifications are network intrusion detection systems (NIDS) and host-based intrusion detection systems (HIDS) . Frequently, intrusion detection systems are divided into two categories based on the detection method [1] . An IDS may be deployed as a physical device or a software application to oversee system operations or network activity. Its principal objective is identifying and responding to malevolent activities or violations of predetermined regulations. IDS varieties demonstrate various applications, encompassing server rooms and enterprise networks. HIDS and NIDS are the primary standard categories. Frequently, classifications of intrusion detection systems are based on their  \ndetection methodologies [2], [3] . The fundamental classification is predicated on signature detection, which compares distinctive patterns in network traffic (e.g., byte sequences) with an established repository of recognized attack signatures. In contrast, the anomaly-based detection approach assesses the current state of a network about a predetermined reference point. This empowers it to identify and discern both established and novel perils. Furthermore, it is critical to note that dimensional reduction","cbCaimT7Uzav9Xwz","https://ap.wps.com/l/cbCaimT7Uzav9Xwz","pdf",504317,1,12,"English","en",105,"# INTRODUCTION\n## Intrusion detection system overview\n## Detection methods: signature vs anomaly\n## Machine learning and preprocessing impact\n## Outliers, missing data, and sampling\n## Normalization and feature selection\n## Classifier evaluation approach","[{\"question\":\"What dataset is used to evaluate the proposed intrusion detection approach?\",\"answer\":\"The study emphasizes the CICIDS 2017 dataset and refines it using stringent preprocessing to improve model readiness and quality.\"},{\"question\":\"Which machine learning classifiers are compared in the study?\",\"answer\":\"The performance of SVM, XGBoost, and naive Bayes is evaluated using multiple metrics.\"},{\"question\":\"How does preprocessing affect intrusion detection effectiveness?\",\"answer\":\"The results emphasize that preprocessing—normalization, class imbalance handling, feature reduction, and feature selection—substantially improves predictive performance and IDS effectiveness.\"}]","Fortifying network security - 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