Strong intrusion detection is considered as a basic requirement for detecting any cyber attack before the breach yield successful outcomes for intruders along with automated prevention or response to such attacks being the next level of requirement. Although various tools and techniques are available for detecting such activities, intruders are still able to intrude heterogeneous environments successfully across the globe. This research work essentially takes the case of various models suggested in this direction, how they get deployed and what appears insufficient in their functioning making it difficult to be implemented. This research work focuses on developing improved models or improving existing models for designing and deployment of network security frameworks and policies, in which functioning of each component and their interconnectivity is taken towards more sufficiency to yield better actions thereby ensuring best possible level of security for all the system present within environment. The fitness functions for the system and which parameters could be used to decide genes for various network events is discussed along with a method to calculate the overall fitness of various network events.

Attack vector, Artificial Intelligence, Crossover, Cyber attack, Cyber Security, Feedback ingestion, Fitness function threshold, Gene, Genetic Algorithm, Intrusion Detection System, Machine Learning, Model, Mutation, Network Event, Network Packet, Network Security, Network Security Policy Framework, Selection, Self-evolutionary

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