Bioinformatics-Inspired Quantized Hard Combination-Based Abnormality Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks

Abstract

String matching algorithms used in bioinformatics can be applied to scenarios in cognitive radios, where reports of cooperative spectrum sensing nodes need to be compared with each other. Cooperative spectrum sensing is susceptible to security risks, where malicious users who participate in the process falsify the spectrum sensing data, thus affecting cognitive radio network performance. In this paper, an efficient spectrum sensing system is developed where each cognitive radio (CR) user senses the spectrum multiple times within an allocated sensing period. Each CR user quantizes its decision to predefined levels so as to achieve a tradeoff between bandwidth utilization and decision reporting accuracy. The reports for all the CR users are compared at the fusion center using Smith-Waterman algorithm, an optimal algorithm for aligning biological sequences used in bioinformatics, and similarity indices are computed. Robust mean and robust deviation of the similarity indices are calculated and a threshold is determined by these values. The CR users who have similarity indices below the given threshold are declared malicious and their reports are discarded. The local decisions of the remaining CR users are combined using the modified rules of decision combination to take a global decision. Simulation results show that our proposed scheme performs better than conventional schemes with and without malicious users. IndexTerms-Bioinformatics, cooperative spectrum sensing, malicious user detection, quantized hard decision, Smith-Waterman algorithm.

Actor–Critic-Algorithm-Based Accurate Spectrum Sensing and Transmission Framework and Energy Conservation in Energy-Constrained Wireless Sensor Network-Based Cognitive Radios

“…e slotted frame structure is considered in [33][34][35][36][37][38][39][40][41]. In this method of spectrum sensing, the time frame is divided into two parts.…”

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

“…A low reliability indicates a malicious user and therefore, it will be excluded from the fusion center (FC)'s final decision about the spectrum. The authors in [31] use a bioinformatics algorithm to propose a cooperative spectrum sensing approach. The sensing nodes which sensed spectrum multiple times in one allocated sensing time slot forward their sensing results to a fusion center that compares them using the bioinformatics algorithm.…”

Lightweight security against combined IE and SSDF attacks in cooperative spectrum sensing for cognitive radio networks

“…Every report is then weighted regarding the similarity with the rest of CUs. The work in is also based on comparing the reports from different users; the authors, however, propose a more advanced algorithm to compare nodes behavior that is based on a biological inspired algorithm. A probabilistic model is suggested in to detect sudden changes in the CUs' behavior.…”