[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119080-en":3,"doc-seo-119080-105":30,"detail-sidebar-cat-0-en-105":92},{"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},119080,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Network Intrusion Detection Based on Machine Learning Classification Algorithms - A Review","The document reviews network intrusion detection systems in the context of the rapid expansion of internet-based threats and the limitations of static signature-based approaches. It highlights the need for systems that can detect and classify malicious activity using machine learning techniques, reducing operational workload while improving detection capability. The review presents an overview of existing machine learning–based IDS frameworks, focusing on algorithmic diversity and their roles in classifying hazardous content accurately.","JISA (JurnalInformatika dan Sains) Vol. 07, No. 01, June 2024  \ne-ISSN: 2614-8404  \np-ISSN : 2776-3234  \nNetwork Intrusion Detection Based on Machine Learning Classification Algorithms: A Review  \nAqeel H.younus1*), Adnan Mohsin Abdulazeez2  \n1Akre University for Applied Sciences/ Technical College of Informatics-Akre/ Department of Information Technology 2Duhok Polytechnic University, Duhok, Kurdistan Region, IRAQ  \n[Email:](Email:1 aqeel.hanash@auas.edu.krd)[1](Email:1 aqeel.hanash@auas.edu.krd)[ aqeel.hanash@auas.edu.krd](Email:1 aqeel.hanash@auas.edu.krd) , [2](2 adnan.mohsin@dpu.edu.krd)[ adnan.mohsin@dpu.edu.krd](2 adnan.mohsin@dpu.edu.krd)  \nAbstract − The worldwide internet continues to spread, presenting numerous escalating hazards with significant potential. Existing static detection systems necessitate frequent updates to signature-based databases and solely detect known malicious threats. Efforts are currently being made to develop network intrusion detection systems that can utilize machine learning techniques to accurately detect and classify hazardous content. This would result in a decrease in the overall workload required. Network Intrusion Detection Systems are created with a diverse range of machine learning algorithms. The objective of the review is to provide a comprehensive overview of the existing machine learning-based intrusion detection systems, with the aim of assisting those involved in the development of network intrusion detection systems..  \nKeywords: Intrusion Detection Systems, Machine learning, SVM, Random Forest.  \nI. INTRODUCTION  \nCurrently, the intrusion detection systems provides a key component when it comes to making sure the systems owners are safe against the cyberthreats. IDS (Intrusion Detection System) is a forms of gather and analyze network data to classify types of attacks[1] . For the network traffice, it is the used of many day-to-day features creation in the form of detecting many types of attacks [2] . Due to the rapid increase in the data that is being generated via the internet in daily life, the industry faces a severe challenge[3] . Datasets are sets ofs situation which includemany features and they are relating to the response of the intrusion detection system[4] . Understanding the type of data that is being collected becomes more important because it has attack types and attributes[5] . The KDD'99 cup is the most widely used dataset for intrusion detection systems. It is used to construct predictive models that can distinguish between different types of intrusions or attacks [6] . The intrusion detection system constructs the model using security datasets such as KDD99 and NSL-KDD [7] . The system has many features, akin to a predictor, that differentiate between normal attacks and aberrant ones. These features are the focus of the system [8] .The categorization model divides the data set into two parts: a training stage and a testing stage [9] . The abundance of characteristics with large dimensions results in intricacy during the training process and consumes valuable time. Hence, it is necessary to carefully choose a subset of valuable and pertinent features from the complete set of features in order to enhance the model's performance during the testing phase [10] . Data preparation is a crucial step in enhancing the quality of a classification model's performance, as stated by machine learning algorithms[11] . The process of solving various forms of large data sets is a highly important phase [12].Machine Learning (ML) techniques, which are commonly employed in computer security data sets, have lately gained popularity in the field of security technology [13] . It  \naids in the examination and management of large volumes of data and identifies the crucial characteristics that are employed in different featureselection strategies [14] . Intrusion Detection System (IDS) is a widely employed machine learning classifier that is utilized to differentiate between differ","cbCairxT77ASHEGR","https://ap.wps.com/l/cbCairxT77ASHEGR","pdf",1015589,1,14,"English","en",105,"# Introduction\n## Intrusion Detection Systems and datasets\n## Machine learning classifiers and evaluation\n# Intrusion Detection System\n## Monitoring, policy violations, and architecture","[{\"question\":\"Why are network intrusion detection systems needed?\",\"answer\":\"They help protect system owners against cyberthreats by monitoring and analyzing network data to identify attack types and security breaches.\"},{\"question\":\"What limitation do static signature-based detection systems have?\",\"answer\":\"They require frequent updates to signature databases and primarily detect known malicious threats rather than evolving ones.\"},{\"question\":\"Which machine learning classifiers are commonly used in intrusion detection?\",\"answer\":\"The document lists several supervised classifiers, including Decision Trees, Naïve Bayes, K-Nearest Neighbor, C4.5, Random Forest, Support Vector Machine, and Logistic Regression.\"}]","Network Intrusion Detection Based on Machine Learning Classification Algorithms - 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