Defense Against Spectrum Sensing Data Falsification Attacks Using Cluster Network Channeling with Distance-Based Attacker Detection in Cognitive Radio Ad-Hoc Networks
Amar Taggu, Bryan Samuel Kharsohnoh · 2023
The growing demand for wireless and smart technologies has led to an unprecedented increase in the utilization of the radio spectrum. However, as the number of users and devices continues to surge, the spectrum is becoming over-crowded, impeding access and efficient utilization. To address this challenge, Cognitive Radio (CR) technology is increasingly becoming the forerunner. Cognitive Radio Ad-Hoc Networks (CRAHNs) consists of cognitive intelligent nodes where the CR nodes operating in an infrastructure-less mode, can sense and hop across different frequencies dynamically. CR nodes adapt broadcast constraints to minimize interference among primary and secondary users. By continuously sensing the radio frequency spectrum, cognitive users identify available licensed channels for communication, selecting optimal channels. CRAHNs are subjected to various security attacks which are unique to such networks only. Spectrum Sensing Data Falsification (SSDF) attack is one such attack where the malicious CR nodes falsify sensing data in an attempt to influence the final fused data. In this work, we simulate a CRAHN consisting of clusters and cluster heads (CH). Apart from implementing routing across the clusters via the CHs, the current work also proposes a distance-based SSDF attacker detection method which operates primarily in the CHs. Comparison with an existing work [1] demonstrates improved detection.