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. This paper presents an analysis of the applicability of neural networks in the identification of instances of external attacks against a network. Research and development of intrusion detection systems has been ongoing since the early 1980’s and the challenges faced by designers increase as the targeted systems because more diverse and complex. Misuse detection is a particularly difficult problem because of the extensive number of vulnerabilities in computer systems and the creativity of the attackers. Neural networks provide a number of advantages in the detection of these attacks. The early results of our tests of these technologies show significant promise, and our future work will involve the refinement of this approach and the development of a full-scale demonstration system.