@article{(Open Science Index):https://publications.waset.org/pdf/5552,
	  title     = {Hybrid Intelligent Intrusion Detection System},
	  author    = {Norbik Bashah and  Idris Bharanidharan Shanmugam and  Abdul Manan Ahmed},
	  country	= {},
	  institution	= {},
	  abstract     = {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 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.},
	    journal   = {International Journal of Humanities and Social Sciences},
	  volume    = {1},
	  number    = {11},
	  year      = {2007},
	  pages     = {3521 - 3524},
	  ee        = {https://publications.waset.org/pdf/5552},
	  url   	= {https://publications.waset.org/vol/11},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 11, 2007},
	}