Performance of p-Norm Detector in Cognitive Radio Networks with Cooperative Spectrum Sensing in Presence of Malicious Users

Nandita Lavanis,Devendra Jalihal

doi:10.1155/2017/4316029

Abstract

A cognitive radio network (CRN) with a cooperative spectrum sensing scheme is considered. This CRN has a primary user and multiple secondary users, some of which are malicious secondary users (MSUs). Energy detection at each SU is performed using a p-norm detector with p≥2, where p=2 corresponds to the standard energy detector. The MSUs are capable of perpetrating spectrum sensing data falsification (SSDF) attacks. At the fusion center (FC), an algorithm is used to suppress these MSUs which could be either an adaptive weighing algorithm or one of the following: Tietjen-Moore (TM) test or Peirce’s criterion. This is followed by computation of a test statistic (TS) which is a random variable. In this paper, we assume TS to have either a Gamma or a Gaussian distribution and calculate the threshold accordingly. We provide closed-form expressions of probability of false alarm and probability of miss-detection under both assumptions. We show that Gaussian assumption of TS is more suited in presence of an SSDF attack when compared with the Gamma assumption. We also compare the detection performance for various values of p and show that p=3 along with the Gaussian assumption is the best amongst all the cases considered.

Highlights

The increase in demand for high-data-rate communication over wireless channels has fueled research in many possible directions
A cognitive radio network (CRN) with cooperative spectrum sensing (CSS) was considered which consisted of multiple malicious users capable of perpetrating sensing data falsification (SSDF) attacks
Closed-form expressions of probability of false alarm and probability of miss-detection for the adaptive weighing algorithm with a p-norm detector were computed under the assumption of a Gamma or Gaussian distribution of the test statistic

Summary

Introduction

The increase in demand for high-data-rate communication over wireless channels has fueled research in many possible directions. The first type of attack is the selfish SSDF or “always yes” attack In this attack, an MSU continuously transmits a signal indicating that PU is present and uses the spectrum without letting other SUs to use it. In an interference SSDF or “always no” attack, an MSU continuously transmits a low energy signal indicating the absence of a PU In this case, other SUs start using the spectrum and cause interference to the PU when the PU is on. Following is the summary: (i) closed expressions of probability of false alarm and probability of miss-detection are provided for MSU removal scheme which uses p-norm detector using assumption of Gamma pdf of TS.

System Model

Algorithms for MSU Suppression

Adaptive Algorithms

Observations and Discussion

Conclusion