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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)
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)
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)
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)
This paper investigates the suitability of linear genetic programming (LGP) technique to model efficient intrusion detection systems, while comparing its performance with artificial neural networks and support vector machines. Due to increasing incidents of cyber attacks and, building effective intrusion detection systems (IDSs) are essential for protecting(More)
Past few years have witnessed a growing recognition of soft computing technologies for the construction of intelligent and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and(More)
Computational Intelligence (CI) methods are increasingly being used for problem solving. This paper concerns using CI-type learning machines for intrusion detection, which is a problem of general interest to transportation infrastructure protection since a necessary task thereof is to protect the computers responsible for the infrastructure's operational(More)