Artificial Neural Networks to Misuse Detection
Event Location / Date(s)
Arlington, VA / 1998
Conference Name / Publication Title
Proceedings of the 21st National Information Systems Security Conference
Misuse detection is the process of attempting to identify instances of network attacks by comparing current activity against the expected actions of an intruder. Most current approaches to misuse detection involve the use of rule-based expert systems to identify indications of known attacks. However, these techniques are less successful in identifying attacks which vary from expected patterns. Artificial neural networks provide the potential to identify and classify network activity based on limited, incomplete, and nonlinear data sources. We present an approach to the process of misuse detection that utilizes the analytical strengths of neural networks, and we provide the results from our preliminary analysis of this approach.
Cannady, James D. Jr., "Artificial Neural Networks to Misuse Detection" (1998). CEC Faculty Proceedings, Presentations, Speeches and Lectures. 563.