Simple, state-based approaches to program-based anomaly detection
ACM Transactions on Information and System Security (TISSEC)
A Real-Time Intrusion Detection System Based on Learning Program Behavior
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
Optimal input design for the detection of changes towards unknown hypotheses
International Journal of Systems Science
Adaptive threshold computation for CUSUM-type procedures in change detection and isolation problems
Computational Statistics & Data Analysis
Tracking stopping times through noisy observations
IEEE Transactions on Information Theory
Quickest change detection of a Markov process across a sensor array
IEEE Transactions on Information Theory
Generalization of sequential Wald's test for more than two hypotheses
MACMESE'10 Proceedings of the 12th WSEAS international conference on Mathematical and computational methods in science and engineering
On sequential discrimination between close markov chains
General Theory of Information Transfer and Combinatorics
| Hi-index | 754.96 |
By using information-theoretic bounds and sequential hypothesis testing theory, this paper provides a new approach to optimal detection of abrupt changes in stochastic systems. This approach not only generalizes previous work in the literature on optimal detection far beyond the relatively simple models treated but also suggests alternative performance criteria which are more tractable and more appropriate for general stochastic systems. In addition, it leads to detection rules which have manageable computational complexity for on-line implementation and yet are nearly optimal under the different performance criteria considered