Graph Analytics for anomaly detection in homogeneous wireless networks - A Simulation Approach

TL;DR

This paper proposes a novel graph-based anomaly detection method for IoT wireless networks, focusing on the behavior of central nodes to identify attack propagation with a simulation approach.

Contribution

It introduces an unexplored methodology leveraging central node behavior for anomaly detection in resource-constrained IoT networks.

Findings

Successful identification of attack propagation in IoT network simulations

Demonstrated effectiveness of central node analysis for anomaly detection

New approach applicable to resource-limited wireless environments

Abstract

In the Internet of Things (IoT) devices are exposed to various kinds of attacks when connected to the Internet. An attack detection mechanism that understands the limitations of these severely resource-constrained devices is necessary. This is important since current approaches are either customized for wireless networks or for the conventional Internet with heavy data transmission. Also, the detection mechanism need not always be as sophisticated. Simply signaling that an attack is taking place may be enough in some situations, for example in NIDS using anomaly detection. In graph networks, central nodes are the nodes that bear the most influence in the network. The purpose of this research is to explore experimentally the relationship between the behavior of central nodes and anomaly detection when an attack spreads through a network. As a result, we propose a novel anomaly detection approach using this unique methodology which has been unexplored so far in communication networks. Also, in the experiment, we identify presence of an attack originating and propagating throughout a network of IoT using our methodology.