[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121162-en":3,"doc-seo-121162-105":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},121162,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Approach for Intrusion Detection System Using Dimensionality Reduction - Feature and Accuracy Improvement","Machine learning and rule-based security can be combined to strengthen intrusion detection in real-time distributed systems where threats evolve continuously. The proposed DR-DBMS model applies dimensionality reduction and feature selection to reduce the feature space, then uses supervised learning with advanced rule-based classifiers to handle different attack types. Simulation results show fast intrusion detection in 0.07 seconds while using fewer features, improving detection accuracy and computational efficiency. The approach targets newly emerging anomalies rather than relying only on fixed signatures.","Machine learning approach for intrusion detection system using  \ndimensionality reduction  \nDeepa Manikandan, Jayaseelan Dhilipan  \nDepartment of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology,  \nRamapuram Campus, Chennai, India  \n\n| Article history:\u003Cbr>Received Jul 15, 2023 Revised Nov 26, 2023 Accepted Nov 29, 2023 |\n| --- |\n| Keywords:\u003Cbr>Database management Dimensionality reduction Feature selection Intrusion detection system Machine learning |\n\nCorresponding Author:  \nAs cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.  \nThis is an open access article under the CC BY-SA license.  \nDeepa Manikandan  \nDepartment of Computer Science and Applications  \nFaculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus Ramapuram, Chennai, Tamil Nadu, India  \nemail: [dm8027@srmist.edu.in](dm8027@srmist.edu.in)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe daily evolution of threats to computer and internet security has forced distributed systems to be embedded with effectivenetwork security challenges. A new set of detection based security solutions is being developed to accommodate and detect these fresh dangers [1], [2] . This system employs supervised machine learning (ML) to create a classifier that can recognize freshly created anomalies and attack variants rather than relying solely on established signatures. To handle the challenge of spotting tiny attacks, the system incorporates a priority resolver and a specially designed iterative algorithm. ML techniques are used to create a classifier that can discriminate between various sorts of attacks [3] . Rule-based classifiers are used to generate a set of conditional branching-based rules like IFTHEN language rules, which are then used by the classifier. Due to the rules that these rule-based classifiers produce, they are regarded as knowledge-based systems.  \nThe area of research in focus revolves around cybersecurity, specifically intrusion detection systems (IDSs) . In today's interconnected world, where organizations heavily rely on computer networks and data exchange, the threat of cyber-attacks has become increasingly prevalent. These detection methods playa crucial role in safeguarding these networks by detecting and preventing unauthorized access, malicious activities, and potential security breaches. The field of IDS involves the development and implementation of techniques and algorithms that can effectively identify and respond to various types of attacks on computer networks. These attacks can range from traditional methods like viruses, worms, and denial-of-service attacks [4]−[6] to more sophisticated threats like advanced persistent threats and zero-day exploits. Traditionally,  \nthese security systems relied on rule-based approaches, which involved defining a set of predefined rules to detect known attack patterns. However, with the rapid advancements in technology and the increasing complexity of cyber-attacks, there is a need for more intelligent and adaptive methods. This has led to the integration of ML and deep learning techniques into IDSs [7] . By leveraging these advanced algorithms, IDSs can learn from past data, identify pat","cbCaitLDwtDyWPXX","https://ap.wps.com/l/cbCaitLDwtDyWPXX","pdf",599912,1,11,"English","en",105,"# Article Info ABSTRACT\n## INTRODUCTION\n## Research Gap Analysis\n## Evaluation of Dimensionality Reduction Techniques\n## Optimization of Classifier Models","[{\"question\":\"What problem does DR-DBMS address in intrusion detection systems?\",\"answer\":\"DR-DBMS targets the difficulty of handling constantly changing cyber threats in real-time distributed environments, where conventional IDS methods struggle with new attack variants.\"},{\"question\":\"How does dimensionality reduction contribute to the proposed system?\",\"answer\":\"Dimensionality reduction reduces the feature space, and feature selection further cuts down the number of features used by the classifier to improve efficiency without sacrificing detection performance.\"},{\"question\":\"What performance result is reported from simulations?\",\"answer\":\"Simulations indicate that the DR-DBMS system detects intrusion attacks in 0.07 seconds while using a smaller number of features.\"}]","Machine Learning Approach for Intrusion Detection System Using Dimensionality Reduction - 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