With the expansion of computer networks, security has become a important issue for computer systems. The threat from attackers and criminal enterprises has grown with the expansion of Internet, therefore, intrusion detection systems have become a core component of computer network due to such threats. In this paper, we present framework integrated with neural network to build an effective intrusion detection system. In paper ANN and Naives Bayes strategies are used to detect the attacks in the system by using dynamic data or real time data. Naives Bayes is used to predict the attacks in the system. In prediction it has to specify that attacks is good or bad. After prediction the data is send to ANN which define the types of attacks are present in system. The systems are compared with existing approaches of intrusion detection which either uses neural network or based on layered framework.
IJCR is following an instant policy on rejection those received papers with plagiarism rate of more than 20%. So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies.
