Polymorphic attack feature validation: bridging the gap between intrusion detection and evolving threats

dc.contributor.advisorHeydari, Shahram S.
dc.contributor.authorBegwani, Raksha
dc.date.accessioned2024-09-24T20:24:18Z
dc.date.available2024-09-24T20:24:18Z
dc.date.issued2024-08-01
dc.description.abstractThis project focuses on enhancing the detection of polymorphic attacks, which can evade traditional Intrusion Detection Systems (IDS) by changing their form with each attack. While IDS are crucial for network security, their effectiveness diminishes against such dynamic threats. The project aims to identify key features exploited by polymorphic attacks, enabling the creation of a feature list to improve detection. Using the SlowHTTP tool for generating attack profiles and the LycoStand tool for essential feature extraction, this research seeks to develop effective mechanisms to analyze polymorphic attacks and its features, addressing the limitations of IDS in identifying these attacks.
dc.identifier.urihttps://ontariotechu.scholaris.ca/handle/10155/1853
dc.language.isoen
dc.subject.otherPolymorphic attack
dc.subject.otherIDS
dc.subject.otherFeature analysis
dc.subject.otherDDoS/DoS
dc.subject.otherFeature extraction tool
dc.titlePolymorphic attack feature validation: bridging the gap between intrusion detection and evolving threats
dc.typeMaster's Project
thesis.degree.disciplineArtificial Intelligence
thesis.degree.grantorUniversity of Ontario Institute of Technology
thesis.degree.nameMaster of IT Security (MITS-AIS)

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