Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks

Abstract

Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc… performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network.

Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks

“…Another approach is to use a deep learning convolution network considering a fourth-order cyclic cumulant vector (CRM) in the frequency domain to detect the modulation patterns in a convolutional deep learning method. It uses an ideal PUE attacker with a limited functionality, and its analysis is for a fixed network using simulations and is not implemented in a real scenario with motional users [26].…”

Machine Learning in NextG Networks via Generative Adversarial Networks

“…While many conventional countermeasures against security attacks in cognitive radio networks exist [172], there is very limited published work on ML techniques for this purpose [173]. Thus, this is a potentially very fruitful research area.…”

Machine Learning-Based Malicious User Detection in Energy Harvested Cognitive Radio-Internet of Things

“…However, the sensing performance, sum rate, and network lifetime were not evaluated. In [25], the authors proposed ML approach for CRNs, where unsupervised ML, supervised ML, semi-supervised ML, deep learning algorithms used to detect the PUEA and improve detection performance. However, the sum rate and the network lifetime were not analyzed.…”