Abstract
| Language | English |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018) |
| Publisher | IEEE |
| Number of pages | 7 |
| ISBN (Electronic) | 9781509060207 |
| ISBN (Print) | 9781509060214 |
| DOIs | |
| Publication status | Published - 15 Oct 2018 |
| Event | IEEE International Conference on Fuzzy Systems - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018 http://www.ecomp.poli.br/~wcci2018/ (Link to Conference Website) |
Conference
| Conference | IEEE International Conference on Fuzzy Systems |
|---|---|
| Abbreviated title | FUZZ-IEEE 2018 |
| Country | Brazil |
| City | Rio de Janeiro |
| Period | 8/07/18 → 13/07/18 |
| Internet address |
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Weighted Fuzzy Rules Optimised by Particle Swarm for Network Intrusion Detection. / Chen, Tianhua; Su, Pan ; Shang, Changjing; Shen, Qiang.
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018). IEEE, 2018. 18166140.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Weighted Fuzzy Rules Optimised by Particle Swarm for Network Intrusion Detection
AU - Chen, Tianhua
AU - Su, Pan
AU - Shang, Changjing
AU - Shen, Qiang
PY - 2018/10/15
Y1 - 2018/10/15
N2 - Network intrusion detection systems (IDSs) dynamically monitor communication events on a network, and decide whether any event is symptomatic of an attack or constitutes a legitimate use of the system. They have become an indispensable component of security infrastructure, e.g., to detect threats before widespread damage takes place. A variety of approaches have been proposed to design IDSs, including fuzzy rule-based techniques that offer advantages such as tolerance of noisy and imprecise data. In particular, fuzzy rules can be highly interpretable and trackable if the underlying fuzzy sets are predefined, directly reflecting domain expertise. This paper proposes such an approach to generate a set of weighted fuzzy rules for building effective IDSs, where the rule weights are optimised by Particle Swarm Optimisation without affecting the underlying predefined fuzzy sets. Experiments are performed on benchmark IDS datasets with comparison to alternative systems built with popular machine learning methods.
AB - Network intrusion detection systems (IDSs) dynamically monitor communication events on a network, and decide whether any event is symptomatic of an attack or constitutes a legitimate use of the system. They have become an indispensable component of security infrastructure, e.g., to detect threats before widespread damage takes place. A variety of approaches have been proposed to design IDSs, including fuzzy rule-based techniques that offer advantages such as tolerance of noisy and imprecise data. In particular, fuzzy rules can be highly interpretable and trackable if the underlying fuzzy sets are predefined, directly reflecting domain expertise. This paper proposes such an approach to generate a set of weighted fuzzy rules for building effective IDSs, where the rule weights are optimised by Particle Swarm Optimisation without affecting the underlying predefined fuzzy sets. Experiments are performed on benchmark IDS datasets with comparison to alternative systems built with popular machine learning methods.
KW - Fuzzy sets
KW - feature extraction
KW - intrusion detection
KW - benchmark testing
KW - training
KW - monitoring
KW - numerical models
KW - fuzzy set theory
KW - learning (artificial intelligence)
KW - Particle swarm optimisation
KW - security of data
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85060446741&origin=resultslist&sort=plf-f&src=s&st1=Weighted+Fuzzy+Rules+Optimised+by+Particle+Swarm+for+Network+Intrusion+&st2=&sid=7e842aae139ba31ca46034c85370b0e3&sot=b&sdt=b&sl=86&s=TITLE-ABS-KEY%28Weighted+Fuzzy+Rules+Optimised+by+Particle+Swarm+for+Network+Intrusion+%29&relpos=0&citeCnt=0&searchTerm=
U2 - 10.1109/FUZZ-IEEE.2018.8491553
DO - 10.1109/FUZZ-IEEE.2018.8491553
M3 - Conference contribution
SN - 9781509060214
BT - 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018)
PB - IEEE
ER -