Reliable Mechanism to Detect Traditional Cyber Attack Using Artificial Neural Networks

DDoS has evolved as the most common and devastating attack that has been confronted from previous years. As many networks reply simultaneously, mostly RREP will work together to accomplish a DDoS attack. Thus, no information system can tolerate and survive once they confront this ruthless attack. There are many existing intrusion detection systems to prevent and protect the system as well as network from DDoS, but still DDoS is complex to perform detection and perplexing. In this research article, an IDS has been developed based on the basics of latency and delays in neural networks. To form a multi-layer architecture, every node is kept on surveillance once the detectors are deployed in the network topology, and the activities of every single node are tracked by their close hop nodes mutually to ensure their status of survival. Only after all of the information is collected in a table, it is forwarded for integrated analysis by their selected expert module. The nodes covered in the first and second layer of firewall experience some suspected packets or streams as that of DDoS pattern and the core expert module that started right after the second firewall will take some effective action and invoke the defense module to ensure the safety of the information system. And the nodes which did not stand against defense module will be isolated first and rebooted later to ensure the normal functionality of the network.