Application Specific NIDS (using unsupervised techniques)

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dc.contributor.authorRatti, Ritesh
dc.date.accessioned2025-01-10T11:54:35Z
dc.date.available2025-01-10T11:54:35Z
dc.date.issued2024
dc.descriptionSupervisors: Nandi, Sukumar and Singh, Sanasam Ranbir
dc.description.abstractIn recent years, the use of unsupervised learning-based methods for network intrusion detection has attracted much attention. Multiple methods using unsupervised mechanisms have been proposed that utilize the information in various formats like network packets, flow information, etc., and use various methods for attack identification. Most of these methods have the limitations on not considering the time factor inherently but explicitly using the time-dependent features for various time windows and considering equal importance for all previous contexts. Also ignoring the fact that each protocol-specific attack is unique and ignoring the protocol awareness to determine attacks. Moreover, considering a single type of view or set of features (network header or flow) to build a machine learning model and ignoring the importance of different views in attack determination. This thesis presents four unsupervised learning-based methods in this direction.
dc.identifier.otherROLL NO.156101013
dc.identifier.urihttps://gyan.iitg.ac.in/handle/123456789/2787
dc.language.isoen
dc.relation.ispartofseriesTH-3418
dc.titleApplication Specific NIDS (using unsupervised techniques)
dc.typeThesis
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PhD Theses (Computer Science and Engineering)