Artificial intelligence techniques applied to intrusion detection

N.B. Idris, B. Shanmugam

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

40 Citations (Scopus)


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, that uses simple data mining techniques to process the network data. The proposed system is a hybrid system that combines anomaly, misuse and host based detection. Simple Fuzzy rules, allow us to construct if-then rules that reflect common ways of describing security attacks. For host based intrusion detection we use neural-networks along with self organizing maps. Suspicious intrusions can be traced back to their 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. © 2005 IEEE.
Original languageEnglish
Title of host publicationProceedings of INDICON 2005: An International Conference of IEEE India Council
Number of pages4
Publication statusPublished - 2005
Externally publishedYes
EventAn International Conference of IEEE India Council - Chennai; India
Duration: 11 Dec 200513 Dec 2005
Conference number: 69141


ConferenceAn International Conference of IEEE India Council
Abbreviated titleINDICON 2005


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