Artificial Intelligence-Based Solutions for the Detection and Mitigation of Jamming and Message Injection Cyberattacks Against Unmanned Aerial Vehicles

Joshua Price, Purdue University

Abstract

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.

Degree

M.Sc.

Advisors

Shamaileh, Purdue University.

Subject Area

Aerospace engineering|Robotics|Transportation

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