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ARTIFICIAL INTELLIGENCE-BASED SOLUTIONS FOR THE DETECTION AND MITIGATION OF JAMMING AND MESSAGE INJECTION CYBERATTACKS AGAINST UNMANNED AERIAL VEHICLES

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thesis
posted on 2023-05-01, 00:13 authored by Joshua Allen PriceJoshua Allen Price

This thesis explores the usage of machine learning (ML) algorithms and software-defined radio (SDR) hardware for the detection of signal jamming and message injection cyberattacks against unmanned aerial vehicle (UAV) wireless communications. In the first work presented in this thesis, a real-time ML solution for classifying four types of jamming attacks is proposed for implementation with a UAV using an onboard Raspberry Pi computer and HackRF One SDR. Also presented in this thesis is a multioutput multiclass convolutional neural network (CNN) model implemented for the purpose of identifying the direction in which a jamming sample is received from, in addition to detecting and classifying the jamming type. Such jamming types studied herein are barrage, single-tone, successive-pulse, and protocol-aware jamming. The findings of this chapter forms the basis of a reinforcement learning (RL) approach for UAV flightpath modification as the next stage of this research. The final work included in this thesis presents a ML solution for the binary classification of three different message injection attacks against ADS-B communication systems, namely path modification, velocity drift and ghost aircraft injection attacks. The collective results of these individual works demonstrate the viability of artificial-intelligence (AI) based solutions for cybersecurity applications with respect to UAV communications.

Funding

Collaborative Research: SaTC: CORE: Small: UAV-NetSAFE.COM: UAV Network Security Assessment and Fidelity Enhancement through Cyber-Attack-Ready Optimized Machine-Learning Platforms

Directorate for Computer & Information Science & Engineering

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History

Degree Type

  • Master of Science in Electrical and Computer Engineering

Department

  • Electrical and Computer Engineering

Campus location

  • Hammond

Advisor/Supervisor/Committee Chair

Khair Al Shamaileh

Additional Committee Member 2

Quamar Niyaz

Additional Committee Member 3

Lizhe Tan