AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies

TL;DR

This paper introduces AGNOSCO, a novel artificial ant colony-based method for identifying infected nodes in networks, addressing communication and computational challenges faced by traditional intrusion detection systems.

Contribution

The paper presents a new ant colony algorithm, AGNOSCO, that efficiently detects infected nodes in networks with reduced communication and computational requirements.

Findings

AGNOSCO effectively identifies infected nodes.

It reduces communication overhead compared to existing methods.

It overcomes computational power limitations.

Abstract

If a computer node is infected by a virus, worm or a backdoor, then this is a security risk for the complete network structure where the node is associated. Existing Network Intrusion Detection Systems (NIDS) provide a certain amount of support for the identification of such infected nodes but suffer from the need of plenty of communication and computational power. In this article, we present a novel approach called AGNOSCO to support the identification of infected nodes through the usage of artificial ant colonies. It is shown that AGNOSCO overcomes the communication and computational power problem while identifying infected nodes properly.