Srinivas Mukkamala

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This paper concerns intrusion detection and audit trail reduction. We describe approaches to intrusion detection and audit data reduction using support vector machines and neural networks. Using a set of benchmark data from the KDD (Knowledge Discovery and Data Mining) competition designed by DARPA, we demonstrate that efficient and highly accurate(More)
Intrusion detection is a critical component of secure information systems. This paper addresses the issue of identifying important input features in building an intrusion detection system (IDS). Since elimination of the insignificant and/or useless inputs leads to a simplification of the problem, faster and more accurate detection may result. Feature(More)
Network forensics is the study of analyzing network activity in order to discover the source of security policy violations or information assurance breaches. Capturing network activity for forensic analysis is simple in theory, but relatively trivial in practice. Not all the information captured or recorded will be useful for analysis. Identifying key(More)
Software security assurance and malware (Trojans, worms, and viruses, etc.) detection are important topics of information security. Software obfuscation, a general technique that is useful for protecting software from reverse engineering, can also be used by hackers to circumvent the malware detection tools. Current static malware detection techniques have(More)
Soft computing techniques are increasingly being used for problem solving. This paper addresses using an ensemble approach of different soft computing and hard computing techniques for intrusion detection. Due to increasing incidents of cyber attacks, building effective intrusion detection systems are essential for protecting information systems security,(More)
This paper describes results concerning the robustness and generalization capabilities of artificial neural networks in detecting intrusions using network audit trails. Through a variety of comparative experiments, it is found that neural network performs the best for intrusion detection. Feature selection is as important for intrusion detection as it is(More)
Computational intelligent techniques can be useful at the diagnosis stage to assist the Oncologist in identifying the malignancy of a tumor. In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computational intelligent techniques for(More)