Enhanced robust cooperative spectrum sensing in cognitive radio

Abstract

As wireless spectrum resources become more scarce while some portions of frequency bands suffer from low utilization, the design of cognitive radio (CR) has recently been urged, which allows opportunistic usage of licensed bands for secondary users without interference with primary users. Spectrum sensing is fundamental for a secondary user to find a specific available spectrum hole. Cooperative spectrum sensing is more accurate and more widely used since it obtains helpful reports from nodes in different locations. However, if some nodes are compromised and report false sensing data to the fusion center on purpose, the accuracy of decisions made by the fusion center can be heavily impaired. Weighted sequential probability ratio test (WSPRT), based on a credit evaluation system to restrict damage caused by malicious nodes, was proposed to address such a spectrum sensing data falsification (SSDF) attack at the price of introducing four times more sampling numbers. In this paper, we propose two new schemes, named enhanced weighted sequential probability ratio test (EWSPRT) and enhanced weighted sequential zero/one test (EWSZOT), which are robust against SSDF attack. By incorporating a new weight module and a new test module, both schemes have much less sampling numbers than WSPRT. Simulation results show that when holding comparable error rates, the numbers of EWSPRT and EWSZOT are 40% and 75% lower than WSPRT, respectively. We also provide theoretical analysis models to support the performance improvement estimates of the new schemes.Index Terms: Cognitive radio (CR), cooperative spectrum sensing, enhanced weighted sequential probability ratio test (EWSPRT), enhanced weighted sequential zero/one test (EWSZOT), spectrum sensing data falsification (SSDF) attack, weighted sequential probability ratio test (WSPRT)

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

“…• Defense efficiency, which includes convergence rate [43] [44], defense cost [45], and computation complexity.…”

Exploiting and defending trust models in cooperative spectrum sensing

“…In the case of SSDF attacks, trust models have been effective at removing malicious sensors from the shared spectrum sensing [13][14][15][16][17]. However, these trust models cannot distinguish between malicious sensors and accurate sensors misled by rogue signals (as opposed to the legitimate primary signal).…”

“…To counter SSDF, various trust models have been proposed to protect CCS from malicious SUs [13][14][15][16][17]. These trust-based CSS protocols build reputation profiles for sensors and filter out the sensing reports from those with low reputations.…”

Security management based on trust determination in cognitive radio networks

“…IndTr kj (10) which reflects the interaction degree of target user compared with other users in the same sub-network.…”

“…Most of the studies in security of CRN focus on spectrum sensing techniques, where the main contributions are attack defense based on attack detection. The existing achievement discussing detailed approaches for the detection and mitigation of specific attacks, such as by Subbalakshmi et al in [5][6][7][8] and other scholars, Chen et al in [3,9], Zhu et al in [10], Yu et al in [11], Wu et al in [12,13]. They all have made a great contribution in the CRN security areas, and their achievements will not be described here, as it is out of the scope of this paper.…”