Idris, Norbik Bashah and Shanmugam, Bharanidharan (2006) Anomaly intrusion detection based on fuzzy logic and data mining. In: Proc. Postgraduate Annual Research Seminar 2006 (PARS 2006) , 2006, UTM.
Official URL: http://comp.utm.my/pars/files/2013/04/Anomaly-Intr...
Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic model Intelligent Intrusion Detection System, based on specific AI approach for intrusion detection. The techniques that are being investigated includes neural networks and fuzzy logic with network profiling, which uses simple data mining techniques to process the network data. The proposed system is a hybrid system that combines anomaly and misuse detection. Simple Fuzzy rules, allow us to construct if-then rules that reflect common ways of describing security attacks. Suspicious intrusions can be traced back to its original source path and any traffic from that particular source will be redirected back to them in future. Both network traffic and system audit data are used as inputs for both the systems.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||data mining, fuzzy logic, intelligent systems network security, anamoly detection, misuse detection|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Liza Porijo|
|Deposited On:||24 Apr 2012 01:20|
|Last Modified:||09 Jun 2014 01:12|
Repository Staff Only: item control page