A Granular Intrusion Detection System Using Rough Cognitive Networks

Gonzalo Nápoles*, Isel Grau, Rafael Falcon, Rafael Bello, Koen Vanhoof

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review


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.
Original languageEnglish
Title of host publicationRecent Advances in Computational Intelligence in Defense and Security
EditorsRami Abielmona, Rafael Falcon, Nur Zincir-Heywood, Hussein A. Abbass
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages23
ISBN (Print)978-3-319-26450-9
Publication statusPublished - 2016
Externally publishedYes


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