TY - GEN
T1 - Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework
AU - Tsang, Chi-Ho
AU - Kwong, Sam
AU - Wang, Hanli
PY - 2005
Y1 - 2005
N2 - In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpretable fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic. © 2005 IEEE.
AB - In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpretable fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic. © 2005 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=34548567506&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-34548567506&origin=recordpage
U2 - 10.1109/ICDM.2005.26
DO - 10.1109/ICDM.2005.26
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0769522785
SN - 9780769522784
SP - 789
EP - 792
BT - Proceedings - IEEE International Conference on Data Mining, ICDM
T2 - 5th IEEE International Conference on Data Mining, ICDM 2005
Y2 - 27 November 2005 through 30 November 2005
ER -