Investigations on detection and prevention of primary user emulation attack in cognitive radio networks using extreme machine learning algorithm

Abstract

Cognitive radio is a competent technology which resolves the spectrum scarcity crisis in a network by allocating free spectrum dynamically and establishes coexistence between the users of the network, without interfering with the incumbent transmission. Primary user emulation attack (PUEA) is one of the major threats to the spectrum sensing performance that decreases the spectrum access probability of the unlicensed users of the network. In this paper Detection and prevention of PUEA is realized proficiently using Time-Distance with signal Strength Evaluation (TDSE) and Extreme Machine Learning (EML) algorithm. TDSE implementation makes it feasible to identify the malicious PUE attacker under mobile user stipulation and EML algorithm provides a swift comparative decision regarding the malicious attack. The technique is also proven to improve the sensing ability, energy efficiency and brings down the overall delay of the system, illustrating a considerable improvement in the overall network performance and spectrum utilization in a cognitive radio network.