NETWORK-BASED INTRUSION DETECTION USING NEURAL NETWORKS
Alan Bivens, Chandrika Palagiri, R.N. Smith, Boleslaw Karol Szymanski, Mark J. Embrechts · 2002
With the growth of computer networking, electronic commerce, and web services, security of networking systems has become very important. Many companies now rely on web services as a major source of revenue. Computer hacking poses significant problems to these companies, as distributed attacks can render their cyber-storefront inoperable for long periods of time. This happens so often, that an entire area of research, called Intrusion Detection, is devoted to detecting this activity. We show that evidence of many of these attacks can be found by a careful analysis of network data. We also illustrate that neural networks can efficiently detect this activity. We test our systems against denial of service attacks, distributed denial of service attacks, and portscans. In this work, we explore network based intrusion detection using classifying, self-organizing maps for data clustering and MLP neural networks for detection.