Intrusion Detection Methodologies Based on Machine Learning:Feature Selection, Datasets, Performance Measures and Results

سال انتشار: 1401
محل انتشار: هفتمین کنفرانس ملی ایده های نو در مهندسی برق
کد COI اختصاصی: NCNIEE07_045
زبان مقاله: انگلیسی
تعداد مشاهده: 261

نویسندگان

Computer Engineering Department, Islamic Azad University (Isfahan branch), Isfahan, Iran

Computer Engineering Department, Naghshejahan Higher Education Institute, Isfahan, Iran

چکیده

Due to the increased need for Internet access in variousindustries and the substantial applications relating to computersthat have been developed recently, cyber-attacks on the Internethave increased, as has the challenge of cyber-security. Thisnecessitated the adoption of an intrusion detection system. Ithandles traffic data in order to keep track of the network and thedevices linked to it in order to spot any malicious activity orattacks on websites or online applications. It has becomeimportant to use an intrusion detection system to inspect datatraffic within the network to assure its confidentiality, integrity,and availability. Despite the researchers' best efforts, IDS stillhas difficulties boosting detection accuracy while lowering falsealarm rates. Therefore, many machine learning techniques havebeen introduced to intrusion detection systems. This paperpresents an overview of the recent literature related to techniquesfor detecting intrusion and cyber-attacks using machine learningalgorithms. And the challenges we face in detecting intrusion inthe network and applying machine learning also shed light on thedata sets used in training the proposed models.

کلیدواژه ها

Cyber Security, Intrusion Detection, CyberAttacks, Supervised Machine Learning, Challenges.

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