A Granular Intrusion Detection System Using Rough Cognitive Networks

Security in computer networks is an active research field since traditional approaches (e.g., access control, encryption, firewalls, etc.) are unable to completely protect networks from attacks and malwares. That is why Intrusion Detection Systems (IDS) have become an essential component of security infrastructure to detect these threats before they inflict widespread damage. Concisely, network intrusion detection is essentially a pattern recognition problem in which network traffic patterns are classified as either normal or abnormal. Several Computational Intelligence (CI) methods have been proposed to solve this challenging problem, including fuzzy sets, swarm intelligence, artificial neural networks and evolutionary computation. Despite the relative success of such methods, the complexity of the classification task associated with intrusion detection demands more effective models. On the other hand, there are scenarios where identifying abnormal patterns could be a challenge as the collected data is still permeated with uncertainty. In this chapter, we tackle the network intrusion detection problem from a classification angle by using a recently proposed granular model named Rough Cognitive Networks (RCN). An RCN is a fuzzy cognitive map that leans upon rough set theory to define its topological constructs. An optimization-based learning mechanism for RCNs is also introduced. The empirical evidence indicates that the RCN is a suitable approach for detecting abnormal traffic patterns in computer networks.