TY - CHAP
T1 - Two sophisticated techniques to improve HMM-based intrusion detection systems
AU - Cho, Sung Bae
AU - Han, Sang Jun
PY - 2003
Y1 - 2003
N2 - Hidden Markov model (HMM) has been successfully applied to anomlay detection as a technique to model normal behavior. Despite its good performance, there are some problems in applying it to real intrusion detection systems: it requires large amount of time to model normal behaviors and the false-positive error rate is relatively high. To remedy these problems, we have proposed two techniques: extracting privilege flows to reduce the normal behaviors and combining multiple models to reduce the false-positive error rate. Experimental results with real audit data show that the proposed method requires significantly shorter time to train HMM without loss of detection rate and significantly reduces the false-positive error rate.
AB - Hidden Markov model (HMM) has been successfully applied to anomlay detection as a technique to model normal behavior. Despite its good performance, there are some problems in applying it to real intrusion detection systems: it requires large amount of time to model normal behaviors and the false-positive error rate is relatively high. To remedy these problems, we have proposed two techniques: extracting privilege flows to reduce the normal behaviors and combining multiple models to reduce the false-positive error rate. Experimental results with real audit data show that the proposed method requires significantly shorter time to train HMM without loss of detection rate and significantly reduces the false-positive error rate.
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U2 - 10.1007/978-3-540-45248-5_12
DO - 10.1007/978-3-540-45248-5_12
M3 - Chapter
AN - SCOPUS:21144432658
SN - 3540408789
SN - 9783540408789
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 207
EP - 219
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Vigna, Giovanni
A2 - Kruegel, Christopher
A2 - Jonsson, Erland
A2 - Kruegel, Christopher
PB - Springer Verlag
ER -