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Erschienen in:
2018 | OriginalPaper | Buchkapitel
An Intelligent Defense and Filtration Platform for Network Traffic
verfasst von : Mehrnoosh Monshizadeh, Vikramajeet Khatri, Buse Atli, Raimo Kantola
Erschienen in: Wired/Wireless Internet Communications
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Abstract
Hybrid Anomaly Detection Model (HADM) is a security platform to detect and prevent cyber-attacks on communication networks. The platform uses a combination of linear and learning algorithms combined with protocol analyzer. The linear algorithms filter and extract distinctive attributes and features of the cyber-attacks while the learning algorithms use these attributes and features to identify new types of cyber-attacks. The protocol analyzer in this platform classifies and filters vulnerable protocols to avoid unnecessary computation load. The use of linear algorithms in conjunction with learning algorithms allows the HADM to achieve improved efficiency in terms of accuracy and computation time in order to detect cyber-attacks over existing solutions.