Intelligent jamming detection‐based robust principal components analysis in communication system for security and defense

Abstract

AbstractThreats to physical layer security from jamming attacks make wireless cognitive communication systems vulnerable. Global Positioning System signal is vulnerable to these attacks. Over the last decade, several types of jamming detection techniques have been proposed, antijamming‐based classical and machine learning (ML) techniques. Most of these techniques are inefficient in detecting jammers. Thus, there is a great need for efficient and quickest jamming detection technique‐based classifier using receiver operating characteristic (ROC) curve for different threshold values with high accuracy. In this work, we compare the efficiency of the proposed orthogonal distance (OD) and score distance (SD) method‐based robust principal component analysis (PCA) in ML classification in detecting jamming signals. Two hypotheses are proposed to distinguish between the presence and absence attack problem. Using this compressed data matrix obtained from modulated wideband converter (MWC) structure via centralized cooperation directly as input of the proposed classifier combined‐based ROC curve for real‐time detection scenarios. The performance of this proposed algorithm‐based robust PCA was evaluated and compared using the detection anomaly rate (DAR%), and false alarm rate (FAR%), area under curve (AUC), and accuracy. The performance of obtained results is good.