A Collaborative Approach for Monitoring Nodes Behavior during Spectrum Sensing to Mitigate Multiple Attacks in Cognitive Radio Networks

Abstract

Spectrum sensing is the first step to overcome the spectrum scarcity problem in Cognitive Radio Networks (CRNs) wherein all unutilized subbands in the radio environment are explored for better spectrum utilization. Adversary nodes can threaten these spectrum sensing results by launching passive and active attacks that prevent legitimate nodes from using the spectrum efficiently. Securing the spectrum sensing process has become an important issue in CRNs in order to ensure reliable and secure spectrum sensing and fair management of resources. In this paper, a novel collaborative approach during spectrum sensing process is proposed. It monitors the behavior of sensing nodes and identifies the malicious and misbehaving sensing nodes. The proposed approach measures the node's sensing reliability using a value called belief level. All the sensing nodes are grouped into a specific number of clusters. In each cluster, a sensing node is selected as a cluster head that is responsible for collecting sensing-reputation reports from different cognitive nodes about each node in the same cluster. The cluster head analyzes information to monitor and judge the nodes' behavior. By simulating the proposed approach, we showed its importance and its efficiency for achieving better spectrum security by mitigating multiple passive and active attacks.

Towards Securing Routing Based on Nodes Behavior During Spectrum Sensing in Cognitive Radio Networks

“…The nodes' BL measures the degree of security of the nodes' behavior during the spectrum sensing phase. The attacker model and capability are like the one that we proposed and used in [43], wherein the node may behave in a malicious, misbehaving, cheating, and/or selfish way to launch different attacks such as PUEA, SSDF, DoS, and objective function attack.…”

“…We simulated our proposed approach under two different simulation scenarios. Tables IV and V show the simulation parameters used referring to [42][43][44][45][46]. In the first simulation scenario shown in Table IV, we compared our proposed routing algorithm with three different state-of-the-art routing algorithms used in CRNs: COADV [19], SEARCH [14], and LAUNCH [28].…”

Adaptive Particle Swarm Optimisation based Energy Efficient Dynamic Correlation Behavior of Secondary Nodes in Cognitive Radio Sensor Networks

“…The interesting part that can be gained from the work relates to the importance of understanding the dynamic behaviour of SUs. In [8,9], the authors come up with energy efficient cooperative network that considers the quality of service for wireless networks in dynamic conditions. The formulation of this approach is based on the multi-channel online optimisation.…”

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

“…Attack in TCP and MAC layers was presented in [19] along with the defence mechanism based on round trip time (RTT) to improve throughput. A collaborative approach was designed in [20] utilising cluster analysis to achieve high spectrum accuracy. Channel hopping‐based defence method was discussed in [21] to lessen the PUEA in CRN.…”