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Analyzing Rules to Detect Attacks in Unauthorized Accesses

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dc.contributor.author Win, Mya Thidar Myo
dc.contributor.author Htun, Phyu Thi
dc.contributor.author Khaing, Kyaw Thet
dc.date.accessioned 2019-07-11T03:41:02Z
dc.date.available 2019-07-11T03:41:02Z
dc.date.issued 2013-02-26
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/690
dc.description.abstract Due to increasing incidents of cyber attacks, building effective intrusion detection systems are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. Current intrusion detection systems (IDS) examine all data features to detect intrusion or misuse patterns and some attacks were detected as normal attacks may be vulnerability the whole system. Some of the features may be redundant or low importance during detection process. This paper utilizes a procedure for analyzing the attack features and developing rules by combining signature analysis with automated techniques to improve readability, comprehensibility, and maintainability of rules. We apply one of the efficient data mining algorithms called random forests for network intrusion detection. Empirical results prove that the proposed method can get the high accuracy in detection the attacks in unauthorized accesses such as warezmaster attack and buffer overflow attack. en_US
dc.language.iso en en_US
dc.publisher Eleventh International Conference On Computer Applications (ICCA 2013) en_US
dc.subject relevant features en_US
dc.subject rules en_US
dc.subject intrusion detection en_US
dc.subject warezmaster en_US
dc.subject buffer_overflow en_US
dc.title Analyzing Rules to Detect Attacks in Unauthorized Accesses en_US
dc.type Article en_US

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