Title :
Adaptive neuro-fuzzy intrusion detection systems
Author :
Chavan, Sampada ; Shah, Khusbu ; Dave, Neha ; Mukherjee, Sanghamitra ; Abraham, Ajith ; Sanyal, Sugata
Author_Institution :
Inst. of Technol. for Women, SNDT Univ., Mumbai, India
Abstract :
The intrusion detection system architecture commonly used in commercial and research systems have a number of problems that limit their configurability, scalability or efficiency. In this paper, two machine-learning paradigms, artificial neural networks and fuzzy inference system, are used to design an intrusion detection system. SNORT is used to perform real time traffic analysis and packet logging on IP network during the training phase of the system. Then a signature pattern database is constructed using protocol analysis and neuro-fuzzy learning method. Using 1998 DARPA Intrusion Detection Evaluation Data and TCP dump raw data, the experiments are deployed and discussed.
Keywords :
IP networks; data privacy; fuzzy neural nets; inference mechanisms; learning (artificial intelligence); message authentication; packet switching; protocols; telecommunication security; telecommunication traffic; 1998 DARPA Intrusion Detection Evaluation Data; IP network; SNORT; TCP dump raw data; adaptive neurofuzzy intrusion detection systems; artificial neural networks; fuzzy inference system; intrusion detection system architecture; machine-learning; neurofuzzy learning; packet logging; protocol analysis; real time traffic analysis; signature pattern database; Artificial neural networks; Databases; Fuzzy neural networks; Fuzzy systems; IP networks; Intrusion detection; Performance analysis; Real time systems; Scalability; Telecommunication traffic;
Conference_Titel :
Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. International Conference on
Print_ISBN :
0-7695-2108-8
DOI :
10.1109/ITCC.2004.1286428
