Who is the wolf in sheep's clothing? a context-aware trust evaluation model for malicious UAV detection during authentication

With the development and widespread application of UAV technology, UAV networks face significant internal threats due to their open and dynamic nature. Internal attackers are those who authenticate with legitimate credentials but act maliciously, akin to “wolves in sheep's clothing”. Numerous studies have confirmed that trust management is an effective way to mitigate insider threats and that the interaction process in UAV networks can usually be divided into an authentication access phase and a task execution phase. However, existing trust evaluation models primarily concentrate on the task execution phase, as the authentication access phase typically relies on cryptography-based identity verification. This approach struggles to identify attackers possessing legitimate credentials during authentication and fails to provide fine-grained access permission allocation for UAVs due to its binary approve/deny nature of authentication. Moreover, these models neglect critical elements in trust evaluation—particularly context-aware information such as the open and dynamic operational environment of UAVs—which significantly impacts evaluation accuracy. To address these issues, this paper proposes a trust evaluation model for UAV authentication that computes trust values during the authentication phase to detect potential attackers, thereby enhancing existing identity authentication mechanisms and supporting fine-grained access decisions based on trust values. The model innovatively integrates local and recommended trust with fine-grained behavioral analysis, while introducing context-aware information—environmental factors—to quantify contextual influences for trust calibration. Theoretical analysis and experimental results demonstrate the model's superior performance in mitigating internal threats, such as on off, blackhole, and energy consumption attacks, compared to state-of-the-art approaches.