Hybrid Framework for Behavioral Prediction of Network Attack Using Honeypot and Dynamic Rule Creation with Different Context for Dynamic Blacklisting

Honeypots are decoys designed to trap, delay, and gather information about attackers. All the previous work in the field was related mainly to intrusion detection system, but in this research work, the highlight is more focused on the novel approach of creation of a Honeypot schema which is powered by intelligence along with the design of classifier. The output generated by the classifier generates a dynamic list of attacks, which are then queued in the proposed Honeypot architecture built with neural network to understand various approach of behavior and patterns of the attacker. The network administrator collects all such relevant information over the network itself allowing the inbound network connection from the attacker to do so and the system creates a hybrid framework to prevent the probability of vulnerable and hostile situation over the network even before the attack event is performed by the attacker.