Fadhila Tlili
ABSTRACT
Industry 4.0 is going through a transitional period via the radically automotive transformations. In particular, unmanned aerial vehicles have significantly contributed to the development of intelligent and connected transportation systems. Thus, the continuous development using diverse technologies to achieve a variety of high-performance services raised the security concerns regarding communicating entities. Thus, being managed by networked controllers, UAVs uses controller area networks (CAN) protocol to broadcast information in a bus. However, this protocol is used as a de facto standard which does not have sufficient security features that raise the security risks. This issue caught the attention of the automotive industry researchers and several studies have attempted to improve the security of the CAN protocol attack detection. However, the proposed studies established general perspective solution and did not pay attention to UAVCAN attack detection. To alleviate these concerns, this paper proposed a dynamic intrusion detection frameworks (DIDF) for UAVCAN. The proposed UAVCAN DIDF scheme adopts an artificial intelligence (AI) based model to achieve high detection performance. We performed experiments using public UAVCAN dataset to evaluate our detection system. The experimental results demonstrate that UAVCAN DIDF has significantly reached a high detection rate with a high true positive and a low false negative rate. The simulation results are encouraging and demonstrate the effectiveness of UAVCAN DIDF.
- Flora Amato, Luigi Coppolino, Francesco Mercaldo, Francesco Moscato, Roberto Nardone, and Antonella Santone. 2021. CAN-Bus Attack Detection With Deep Learning. IEEE Transactions on Intelligent Transportation Systems 22, 8 (2021), 5081–5090. https://doi.org/10.1109/TITS.2020.3046974Google Scholar
Digital Library
- Mehmet Bozdal, Mohammad Samie, Sohaib Aslam, and Ian Jennions. 2020. Evaluation of can bus security challenges. Sensors 20, 8 (2020), 2364.Google ScholarCross Ref
- Huy Kang Kim (cenda at korea.ac.kr). [n. d.]. UAVCAN ATTACK DATASET. https://ocslab.hksecurity.net/Datasets/uavcan-attack-datasetGoogle Scholar
- Araya Kibrom Desta, Shuji Ohira, Ismail Arai, and Kazutoshi Fujikawa. 2020. ID sequence analysis for intrusion detection in the CAN bus using long short term memory networks. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 1–6.Google ScholarCross Ref
- Mabrouka Gmiden, Mohamed Hedi Gmiden, and Hafedh Trabelsi. 2016. An intrusion detection method for securing in-vehicle CAN bus. In 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA). IEEE, 176–180.Google ScholarCross Ref
- Achref Haddaji, Samiha Ayed, and Lamia Chaari. 2022. Federated learning with blockchain approach for trust management in IoV. In Advanced Information Networking and Applications: Proceedings of the 36th International Conference on Advanced Information Networking and Applications (AINA-2022), Volume 1. Springer, 411–423.Google ScholarCross Ref
- Achref Haddaji, Samiha Ayed, and Lamia Chaari Fourati. 2022. Artificial Intelligence techniques to mitigate cyber-attacks within vehicular networks: Survey. Computers and Electrical Engineering 104 (2022), 108460.Google ScholarDigital Library
- Achref Haddaji, Samiha Ayed, and Lamia Chaari Fourati. 2023. A Transfer Learning Based Intrusion Detection System for Internet of Vehicles. In 2023 15th International Conference on Developments in eSystems Engineering (DeSE). 533–539. https://doi.org/10.1109/DeSE58274.2023.10099623Google ScholarCross Ref
- Dongsung Kim, Yuchan Song, Soonhyeon Kwon, Haerin Kim, Jeong Do Yoo, and Huy Kang Kim. 2022. UAVCAN Dataset Description. arXiv preprint arXiv:2212.09268 (2022).Google Scholar
- Bin Li, Zesong Fei, and Yan Zhang. 2018. UAV communications for 5G and beyond: Recent advances and future trends. IEEE Internet of Things Journal 6, 2 (2018), 2241–2263.Google ScholarCross Ref
- T Madhuvanthi and S Velliangiri. 2023. Secure Communication for Unmanned Aerial Vehicles. In 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 217–221.Google Scholar
- Almotery Ossamah. 2020. Blockchain as a solution to Drone Cybersecurity. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE, 1–9.Google Scholar
- Peter Pekarčík, Eva Chovancová, Martin Havrilla, and Martin Hasin. 2023. Security analysis of attacks on UAV. In 2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI). 000057–000062. https://doi.org/10.1109/SAMI58000.2023.10044500Google ScholarCross Ref
- Niranjan Ravi and Mohamed El-Sharkawy. 2019. Integration of UAVs with Real Time Operating Systems using UAVCAN. In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). 0600–0605. https://doi.org/10.1109/UEMCON47517.2019.8993011Google ScholarCross Ref
- Niranjan Ravi and Mohamed El-Sharkawy. 2019. Integration of UAVs with real time operating systems using UAVCAN. In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). IEEE, 0600–0605.Google Scholar
- Niranjan Ravi and Mohamed El-Sharkawy. 2021. Enhanced Data Transportation in Remote Locations Using UAV Aided Edge Computing. (2021).Google Scholar
- Hyun Min Song, Jiyoung Woo, and Huy Kang Kim. 2020. In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications 21 (2020), 100198.Google ScholarDigital Library
- Fadhila Tlili, Lamia Chaari Fourati, Samiha Ayed, and Bassem Ouni. 2022. Investigation on vulnerabilities, threats and attacks prohibiting UAVs charging and depleting UAVs batteries: Assessments countermeasures. Ad Hoc Networks 129 (2022), 102805.Google ScholarDigital Library
- Safi Ullah, Muazzam A Khan, Jawad Ahmad, Sajjad Shaukat Jamal, Zil e Huma, Muhammad Tahir Hassan, Nikolaos Pitropakis, and William J Buchanan. 2022. HDL-IDS: A hybrid deep learning architecture for intrusion detection in the internet of vehicles. Sensors 22, 4 (2022), 1340.Google ScholarCross Ref
Index Terms
- Dynamic Intrusion Detection Framework for UAVCAN Protocol Using AI
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