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The network management function includes monitoring the activities of all network devices and the activities of the user. It also logs the anomalous or malicious activity of the user called intrusions. Normally the administrator would detect network problems or intrusions by analyzing the information in the log file at regular intervals of time. It is proposed to use aural signaling of malicious events. Thus a suitable auralization of all the modules in a network management application would allow a network administrator to take the necessary corrective actions. The goal of this research is to investigate usefulness of auralization in intrusion detection. The work focuses on the auralization of an intrusion detection system and an experimental determination of the effectiveness of the auralization performed. The auralization would allow the administrator to detect some attacks like Denial of service, which would assist in providing better service to the legitimate users by thwarting the intrusions immediately. The work attempts to find the effectiveness of auralization in early detection of false positives as well as in monitoring intruders and vulnerable hosts in a timely manner.

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His research interests are in the areas of computer network security, machine learning, data mining, information acquisition, and decision theory. Specifically, in active learning, active inference, statistical relational learning, collective classification, and probabilistic graphical models.

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