Abstract
A non-predicted defect in Unmanned Aerial Vehicles (UAVs) could occur provisionally during their deployment process. Consequently, it is critical to optimize the detection of these instances. More specifically, the deviations in normal behavior indicate the possibility of triggered attacks, failures, and flaws. Therefore, intrusion detection (ID) is mandatory for UAVs security. Meanwhile, ID performance remains an arguable problem. Most of IDSs are applied for one predefined application. There is no general model for accurately detecting both anomalies and faults. To investigate this issue, this paper presents a new dynamic approach for UAVs fault and anomaly detection to investigate this issue. To resolve the drawbacks mentioned above, we propose an attack and fault detection approach. Our method shows better performance using a large dataset, trained on only a small fraction corresponding to normal flight strategy.
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Tlili, F., Ayed, S., Chaari, L., Ouni, B. (2022). Artificial Intelligence Based Approach for Fault and Anomaly Detection Within UAVs. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-030-99584-3_26
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DOI: https://doi.org/10.1007/978-3-030-99584-3_26
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