Abstract—In this paper we introduce the second generation of the experimental detection framework of AIPS system which is used for experimentation with detection models and with their combinations. Our research aims mainly on detection of attacks that abuse vulnerabilities of buffer overflow type, but the final goal is to extend detection techniques to cover various types of vulnerabilities. This article describes the concept of detection framework, updated set of network metrics, provides a design of model architecture and shows an experimental results with draft of framework on the set of laboratory simulated attacks.
Index Terms—Artificial intelligence, behavioral signatures, metrics, network security, security, security design.
Authors are all with Faculty of Information Technology, Brno University of Technology, Czech republic (e-mail: ibarabas@fit.vutbr.cz, ihomoliak@fit.vutbr.cz, idrozd@fit.vutbr.cz, hanacek@fit.vutbr.cz).
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Cite: Maros Barabas, Ivan Homoliak, Michal Drozd, and Petr Hanacek, "Automated Malware Detection Based on Novel Network Behavioral Signatures," International Journal of Engineering and Technology vol. 5, no. 2, pp. 249-253, 2013.
