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MaDe: Malicious Aerial Vehicle Detection using Generalized Likelihood Ratio Test
The use of unmanned aerial vehicles (UAVs) for diverse activities has increased rapidly in recent years. Nonetheless, if operational cyber security is not handled effectively, these technologies offer a significant hazard which can cause catastrophic harm. Therefore, it is important to identify the potential attacks that can be implemented by an adversary. Traditional methods for data integrity designed for the Internet are not suitable for UAV assisted vehicular or wireless sensor networks due to the high communication overhead and latency required. This paper proposes a lightweight data integrity technique called MaDe to address this problem. Every device, at regular intervals, generates an authentication parameter that depends on the packets transmitted. The authentication parameters are only delivered to a central server or the device where the integrity of the packets is verified. At the server, MaDe takes the final decision about an UAV using a generalized likelihood ratio test. MaDe can identify malicious UAVs effectively as demonstrated through our performance analysis. The results show that MaDe detects malicious UAVs with minimum communication overhead and latency.