[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119531-en":3,"doc-seo-119531-105":30,"detail-sidebar-cat-0-en-105":95},{"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},119531,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Effectiveness of Machine Learning Models in Intrusion Detection Systems - A Systematic Review","The review examines how machine learning models contribute to intrusion detection, focusing on effectiveness despite challenges such as time-span constraints and differences in classification within intrusion detection systems. A meta-synthesis based systematic literature review design is applied using PRISMA-guided selection and data extraction from nineteen final studies. The study evaluates common ML models and reports performance metrics used to assess ML-enhanced intrusion detection.","Effectiveness of Machine Learning Models in Intrusion Detection Systems: A Systematic Review  \nName: Dahunsi Samuel Adeyemi  \nReceived: 27 July 2024/Accepted: 19 October 2024/Published: 26 October 2024  \nAbstract While there are several benefits of learning models have the capacity to detect machine learning (ML) algorithm for intrusion various cyber intrusions.  \ndetection, it has been established that there are  \nother issues like time span and classification of Keywords: Machine learning, deep learning, data. Thus, this study conducted a systematic intrusion detection systems, effectiveness, review on the effectiveness of machine learning  intrusion detection  models in intrusion detection systems. Using  \nthe meta-synthesis research design, the study Dahunsi Samuel Adeyemi  \nadopts a systematic literature review University of Central Missouri, Missouri, US approach. Different databases (Web [of](of Email: dxa26930@ucmo.edu)[ Email: ](of Email: dxa26930@ucmo.edu)[dxa26930@ucmo.edu](of Email: dxa26930@ucmo.edu)  \n[Science](Science), Scopus, Google Scholar, IEEE Orcid id: 0009-0007-5485-8052 Xplore, and CINAHL) were consulted and the 1.0 Introduction  \nsearch techniques required the use of Preferred Generally, intrusion detection systems can be Reporting Items for Systematic Reviews and referred to as efforts to prevent intrusions or Meta-analysis (PRISMA). Data were extracted attacks that can damage the credibility of from the nineteen final selected studies, using security services, such as data confidentiality, the data extraction table. Results showed that integrity, and availability. These systems can the commonly used ML models include be categorized into two, which include Random Forest (RF), Support Vector Machine Signature-based Intrusion Detection Systems (SVM), Decision Trees (DT), Naïve Bayes (SIDS) and Anomaly-based Intrusion (NB), K-Nearest Neighbors (KNN), Logistic Detection Systems (AIDS) (Khraisat et al., Regression (LR), Gradient Boosting, and 2019) . Essentially, the relevance and AdaBoost. Findings showed that the usefulness of any system can be entrenched performance metrics used to measure the when it is secured. Milenkoski et al. (2015) effectiveness of ML-enhanced intrusion noted that intrusion detection systems are also detection systems include accuracy, precision, known as computer intrusion detection recall, F1-score, error margin, false positive systems. The authors noted that intrusion rate (FPR), false negative rate (FNR), and area detection systems can be considered secure under the ROC curve (AUC). It was when they possess the qualities of demonstrated that ML algorithms perform well confidentiality, integrity, and availability of in detecting various cyber intrusions. The their data and services. Thus, the achievement datasets used for training machine learning of a secure system should be paramount to a models include KDD Cup 99, NSL-KDD, system developer.  \nUNSW-NB15, Kyoto, CICIDS2017, and Ordinarily, system attacks are often intentional Wireless Sensor Network Dataset (WSN-DS). attempts to compromise the system’s security The challenges associated with the application features and architectures (Ahmadjee et al., of ML algorithms for intrusion detection 2022) . Therefore, system developers and users systems include data imbalance, high need to ensure they detect such system attacks dimensionality, and feature selection in order to achieve or enjoy credible systems. complexities. The study concludes that machine This accentuates the significance of intrusion  \ndetection systems. Ozkan-Okay et al. (2021) described an intrusion detection system as a process of keeping an eye on activities taking place on a computer system or network and examining them for indications of possible dangers, such as threats or infractions of usage guidelines or accepted security procedures. The authors noted further that the main reasons for the use of intrusion detection systems (IDS) include potential event det","cbCaifGTSRoEuuus","https://ap.wps.com/l/cbCaifGTSRoEuuus","pdf",411953,1,22,"English","en",105,"# Introduction\n## Intrusion Detection Systems and Their Purpose\n## Machine Learning Models for Intrusion Detection\n## Datasets Used for Training\n## Challenges in Applying ML for IDS\n## Effectiveness Metrics for ML-Enhanced IDS","[{\"question\":\"What approach does the study use to evaluate machine learning models for intrusion detection?\",\"answer\":\"It uses a meta-synthesis design as a systematic literature review, guided by PRISMA, extracting data from nineteen final selected studies.\"},{\"question\":\"Which machine learning models are commonly used in intrusion detection systems according to the review?\",\"answer\":\"The review highlights models such as Random Forest, Support Vector Machine, Decision Trees, Naïve Bayes, K-Nearest Neighbors, Logistic Regression, Gradient Boosting, and AdaBoost.\"},{\"question\":\"What metrics are used to measure the effectiveness of ML-enhanced intrusion detection systems?\",\"answer\":\"Effectiveness is assessed using accuracy, precision, recall, F1-score, error margin, false positive rate, false negative rate, and ROC curve area (AUC).\"},{\"question\":\"What challenges are associated with applying machine learning algorithms to intrusion detection?\",\"answer\":\"Key challenges include data imbalance, high dimensionality, and feature selection complexity for reliable model performance.\"}]","Effectiveness of Machine Learning Models in Intrusion Detection Systems - 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