Published Paper Details:

Network security has become a vital issue in present scenario, as it is very important to maintain high-level security to ensure safety and trusted communication of information in a computer network. Presently data communication between network and host is leading to a different number of intrusion activities and misuses. Thus, to provide a solution for such malicious attacks, the Intrusion Detection System (IDS) came into existence and held the attention of the computer science field. There are various approaches to design an IDS. Though there is several existing literature on IDS issues, still they pose some disadvantages. It is important for IDS, to detect previously known attacks with high accuracy and unseen attacks to minimize the losses and their impact at an early stage. The process of selecting the best feature is a vital role to ensure the performance, speed, accuracy, and reliability of the detector. Here, in this research study we will try to improve detection accuracy and efficiency for hybrid IDS model based on feature optimization method using multilayer neural network (ML-NN).