Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter

TL;DR

This paper introduces a Kalman filter-based detection method for primary user emulation attacks in cognitive radio networks, effectively handling scenarios with mobile primary users and demonstrating improved detection through simulations.

Contribution

A novel Kalman filter-based approach for detecting primary user emulation attacks with mobile primary users in cognitive radio networks.

Findings

Effective detection of mobile primary user emulation attacks.

Simulation results show improved accuracy over existing methods.

Handles non-stationary primary user scenarios.

Abstract

Cognitive radio technology addresses the problem of spectrum scarcity by allowing secondary users to use the vacant spectrum bands without causing interference to the primary users. However, several attacks could disturb the normal functioning of the cognitive radio network. Primary user emulation attacks are one of the most severe attacks in which a malicious user emulates the primary user signal characteristics to either prevent other legitimate secondary users from accessing the idle channels or causing harmful interference to the primary users. There are several proposed approaches to detect the primary user emulation attackers. However, most of these techniques assume that the primary user location is fixed, which does not make them valid when the primary user is mobile. In this paper, we propose a new approach based on the Kalman filter framework for detecting the primary user emulation attacks with a non-stationary primary user. Several experiments have been conducted and the advantages of the proposed approach are demonstrated through the simulation results.