Channel Footprint-Based Detection of Topology Attacks in IoT Networks

Topological attacks, including Sybil and wormhole attacks, modify a network topology by adding fake nodes and links to bypass typical routing paths, significantly degrading the Internet of Things (IoT) network performance. The presence of fake nodes and links introduces inconsistencies between the modified topology and the actual channel states of the nodes. Therefore, a fast and precise method for detecting topological attacks is crucial to identify and count these inconsistencies, which reveal the presence of an attack and affected nodes. In this paper, we propose a straightforward numerical metric called the Number of Channel Footprint Inconsistencies (NCFI) to count inconsistencies between nodes' transmission, reception, and idle states on the channel and the network topology. In the proposed method, each node reports its channel states to a central node, which uses a Constraint Satisfaction Problem (CSP)-based approach in graph coloring to calculate the NCFI. Additionally, each pair of IoT nodes can use the NCFI in a distributed manner to detect the presence of fake links between them. Our simulation results indicate that the proposed method can accurately detect the presence of attackers in the network compared to the state-of-the-art methods.