<p>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.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22723922 |
| Date | 01 May 2023 |
| Creators | Joshua Allen Price (15379817) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/ARTIFICIAL_INTELLIGENCE-BASED_SOLUTIONS_FOR_THE_DETECTION_AND_MITIGATION_OF_JAMMING_AND_MESSAGE_INJECTION_CYBERATTACKS_AGAINST_UNMANNED_AERIAL_VEHICLES/22723922 |
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