Title :
Novel intrusion detection system integrating layered framework with neural network
Author :
Srivastav, N. ; Challa, R.K.
Author_Institution :
Dept. of Comput. Sci. & Eng., NITTTR, Chandigarh, India
Abstract :
The threat from spammers, attackers and criminal enterprises has grown with the expansion of Internet, thus, intrusion detection systems (IDS)have become a core component of computer network due to prevalence of such threats. In this paper, we present layered framework integrated with neural network to build an effective intrusion detection system. This system has experimented with Knowledge Discovery & Data Mining(KDD) 1999 dataset. The systems are compared with existing approaches of intrusion detection which either uses neural network or based on layered framework. The results show that the proposed system has high attack detection accuracy and less false alarm rate.
Keywords :
Internet; computer network security; data mining; neural nets; IDS; Internet; KDD 1999 dataset; Knowledge Discovery and Data Mining 1999 dataset; attack detection accuracy; attackers; computer network; criminal enterprises; false alarm rate; intrusion detection systems; layered framework; neural network; spammers; Biological neural networks; Computational modeling; Feature extraction; Intrusion detection; Probes; Training; IDS; KDD cup99 dataset; layered framework; neural network;
Conference_Titel :
Advance Computing Conference (IACC), 2013 IEEE 3rd International
Conference_Location :
Ghaziabad
Print_ISBN :
978-1-4673-4527-9
DOI :
10.1109/IAdCC.2013.6514309