Learning Self-Awareness Models for Physical Layer Security in Cognitive and AI-enabled Radios

TL;DR

This paper proposes a data-driven Self-Awareness module for Cognitive Radios that enhances physical layer security by detecting and predicting malicious attacks, outperforming traditional methods and improving explainability and learning efficiency.

Contribution

It introduces a novel hierarchical, incrementally growing Self-Awareness module for CRs, advancing security and learning capabilities in AI-enabled radios.

Findings

High accuracy in detecting and classifying jamming attacks.

Outperforms conventional energy detectors and deep learning classifiers.

Achieves faster convergence in learning attack strategies.

Abstract

Cognitive Radio (CR) is a paradigm shift in wireless communications to resolve the spectrum scarcity issue with the ability to self-organize, self-plan and self-regulate. On the other hand, wireless devices that can learn from their environment can also be taught things by malicious elements of their environment, and hence, malicious attacks are a great concern in the CR, especially for physical layer security. This thesis introduces a data-driven Self-Awareness (SA) module in CR that can support the system to establish secure networks against various attacks from malicious users. Such users can manipulate the radio spectrum to make the CR learn wrong behaviours and take mistaken actions. The SA module consists of several functionalities that allow the radio to learn a hierarchical representation of the environment and grow its long-term memory incrementally. Therefore, this novel SA module is a way forward towards realizing the original vision of CR (i.e. Mitola's Radio) and AI-enabled radios. Experimental results show that introducing the novel SA functionalities provides the high accuracy of characterizing, detecting, classifying and predicting the jammer's activities and outperforms conventional detection methods such as Energy detectors and advanced classification methods such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Stacked Autoencoder (SAE). It also verifies that the proposed approach achieves a higher degree of explainability than deep learning techniques and verifies the capability to learn an efficient strategy to avoid future attacks with higher convergence speed compared to conventional Frequency Hopping and Q-learning.