Intrusion Detection Systems By Anamoly-Based Using Neural Network

To improve network security different steps has been taken as size and importance of the network has increases day by day. Then chances of a network attacks increases Network is mainly attacked by some intrusions that are identified by network intrusion detection system. These intrusions are mainly present in data packets and each packet has to scan for its detection. This paper works to develop a intrusion detection system which utilizes the identity and signature of the intrusion for identifying different kinds of intrusions. As network intrusion detection system need to be efficient enough that chance of false alarm generation should be less, which means identifying as a intrusion but actually it is not an intrusion. Result obtained after analyzing this system is quite good enough that nearly 90% of true alarms are generated. It detect intrusion for various services like Dos, SSH, etc by neural network.