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Artificial Intelligence Based Approach for Fault and Anomaly Detection Within UAVs

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Advanced Information Networking and Applications (AINA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 449))

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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Author information

Authors and Affiliations

  1. National School of Electronics and Telecommunications of Sfax, Sfax, Tunisia

    Fadhila Tlili

  2. LIST3N Laboratory, University of Technology of Troyes, Troyes, France

    Fadhila Tlili & Samiha Ayed

  3. Digital Research Center of Sfax (CRNS), Laboratory of Signals, systeMs aRtificial Intelligence, neTworkS (SM@RTS), Sfax University, Sfax, Tunisia

    Fadhila Tlili & Lamia Chaari

  4. Technology Innovation Institute, Abu Dhabi, United Arab Emirates

    Bassem Ouni

Authors
  1. Fadhila Tlili
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  2. Samiha Ayed
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  3. Lamia Chaari
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  4. Bassem Ouni
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Corresponding author

Correspondence to Fadhila Tlili .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Technology Sydney, Sydney, NSW, Australia

    Farookh Hussain

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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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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