Adaptive Critic Design for Computer Intrusion Detection System
Abstract
This paper summarizes ongoing research. A neural network is used to detect a computer system intrusion basing on data from the system audit trail generated by Solaris Basic Security Module. The data have been provided by Lincoln Labs, MIT. The system alerts the human operator, when it encounters suspicious activity logged in the audit trail. To reduce the false alarm rate and accommodate the temporal indefiniteness of moment of attack a reinforcement learning approach is chosen to train the network.
Recommended Citation
A. Novokhodko et al., "Adaptive Critic Design for Computer Intrusion Detection System," SPIE Proceedings of Applications and Science of Computational Intelligence IV, SPIE -- The International Society for Optical Engineering, Apr 2001.
The definitive version is available at https://doi.org/10.1117/12.421156
Department(s)
Electrical and Computer Engineering
Second Department
Engineering Management and Systems Engineering
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2001 SPIE -- The International Society for Optical Engineering, All rights reserved.
Publication Date
01 Apr 2001
