Efficient Intrusion Detection System Using Enhanced MLP and Optimized MLP Heuristic Approach

Abstract

newline A network contains three main components like system, hardware, and software. As the field of the internet has widened its wings among all the areas of the global market, it is very much significant to ensure the security of the network from being infringed by intruders. The new security difficulties and risks happen daily in the cloud framework. Most of the prevailing IDS and IPS are constructed to look after various types of threats. Though these systems address the problem of network security, there is no single system to authenticate the security of systems from future threats. With the growing demand for anomaly-based IDSs, numerous strategies and programs are deliberated to track the newly rising assaults on network systems. The main goal of IDPs was to recognize any type of incidents that occur in the system. The IPS could change the basic configuration of any other security system to deny an attack. An IDPS system helps in monitoring a single unit or a network for malicious activities or suspicious behaviors and leads to a remedial action against it. Every IDS system can be designed at any place in the network to perform the sole work of identifying anomaly ventures. IDPs methodologies make use of different techniques to detect threats. Anomaly identification approaches are framed concerning the creation of a behavior profile executed in the system for consistent updating. A wireless IDPS observes the traffic in the wireless network and investigates the wireless networking protocols to identify the suspicious activities indulged in the protocols. Now a day, the number of methods has become very popular which leads to confusion in choosing the apt methodology that deploys to secure the system. The stored data are exposed to cyber-attack and security threats and face difficulty in reliability. Due to the nature of the network, security has become a threat to employing a wireless network. There exist different kinds of IDPS techniques, that are segregated initially based on the types of events