Abstract
| Original language | English |
|---|---|
| Title of host publication | SoCPaR 2009 - Soft Computing and Pattern Recognition |
| Pages | 212-217 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2009 |
| Externally published | Yes |
| Event | International Conference on Soft Computing and Pattern Recognition - Malacca; Malaysia Duration: 4 Dec 2009 → 7 Dec 2009 |
Conference
| Conference | International Conference on Soft Computing and Pattern Recognition |
|---|---|
| Abbreviated title | SoCPaR 2009 |
| Period | 4/12/09 → 7/12/09 |
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Improved intrusion detection system using fuzzy logic for detecting anamoly and misuse type of attacks. / Shanmugam, B.; Idris, N.B.
SoCPaR 2009 - Soft Computing and Pattern Recognition. 2009. p. 212-217.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › Research › peer-review
TY - GEN
T1 - Improved intrusion detection system using fuzzy logic for detecting anamoly and misuse type of attacks
AU - Shanmugam, B.
AU - Idris, N.B.
PY - 2009
Y1 - 2009
N2 - Currently available intrusion detection systems focus mainly on determining uncharacteristic system events in distributed networks using signature based approach. Due to its limitation of finding novel attacks, we propose a hybrid model based on improved fuzzy and data mining techniques, which can detect both misuse and anomaly attacks. The aim of our research is to reduce the amount of data retained for processing i.e., attribute selection process and also to improve the detection rate of the existing IDS using data mining technique. We then use improved Kuok fuzzy data mining algorithm, which in turn a modified version of APRIORI algorithm, for implementing fuzzy rules, which allows us to construct if-then rules that reflect common ways of describing security attacks. We applied fuzzy inference engine using mamdani inference mechanism with three variable inputs for faster decision making. The proposed model has been tested and benchmarked against DARPA 1999 data set for its efficiency and also tested against the "live" networking environment inside the campus and the results has been discussed. © 2009 IEEE.
AB - Currently available intrusion detection systems focus mainly on determining uncharacteristic system events in distributed networks using signature based approach. Due to its limitation of finding novel attacks, we propose a hybrid model based on improved fuzzy and data mining techniques, which can detect both misuse and anomaly attacks. The aim of our research is to reduce the amount of data retained for processing i.e., attribute selection process and also to improve the detection rate of the existing IDS using data mining technique. We then use improved Kuok fuzzy data mining algorithm, which in turn a modified version of APRIORI algorithm, for implementing fuzzy rules, which allows us to construct if-then rules that reflect common ways of describing security attacks. We applied fuzzy inference engine using mamdani inference mechanism with three variable inputs for faster decision making. The proposed model has been tested and benchmarked against DARPA 1999 data set for its efficiency and also tested against the "live" networking environment inside the campus and the results has been discussed. © 2009 IEEE.
KW - Apriori
KW - Fuzzy logic
KW - Hybrid system
KW - Intrusion detection
KW - Apriori algorithms
KW - Attribute selection
KW - Data mining techniques
KW - Data sets
KW - Detection rates
KW - Distributed networks
KW - Fuzzy-data mining
KW - Hybrid model
KW - If-then rules
KW - Intrusion Detection Systems
KW - Mamdani inference
KW - Networking environment
KW - Security attacks
KW - Signature-based approach
KW - Computer crime
KW - Data mining
KW - Data processing
KW - Electric grounding
KW - Fuzzy inference
KW - Fuzzy systems
KW - Hybrid computers
KW - Hybrid systems
KW - Industrial research
KW - Pattern recognition
KW - Soft computing
U2 - 10.1109/SoCPaR.2009.51
DO - 10.1109/SoCPaR.2009.51
M3 - Conference Paper published in Proceedings
SN - 978-076953879-2
SP - 212
EP - 217
BT - SoCPaR 2009 - Soft Computing and Pattern Recognition
ER -