Abstract
Recently, Unmanned Aerial Vehicles (UAVs) have become a widely popular technology with remarkable growth and unprecedented attention. However, UAV communication networks are susceptible to various cyber-intrusions/threats due to their limited computation and communication capabilities. Such intrusions/misbehaviors tend to be processed as normal packets through the UAV communication networks. In this work, we present an autonomous intrusion detection system that can efficiently detect the malicious threats invading UAV using deep convolutional neural networks (UAV-IDS-ConvNet). Specifically, the proposed system considers encrypted Wi-Fi traffic data records of three types of commonly used UAVs: Parrot Bebop UAVs, DBPower UDI UAVs, and DJI Spark UAVs. To evaluate the developed system, we employed the UAV-IDS-2020 dataset which includes various attacks on UAV networks in unidirectional and bidirectional communication flow modes. Moreover, we emulate the context of homogeneous and heterogeneous networked UAVs. Our best experimental outcomes exhibited a victorious intrusion detection accuracy of 99.50% for the two-class classifier model (normal UAV vs. anomaly) with 2.77 ms prediction time. Besides, the proposed system was evaluated using other performance metrics including confusion matrix parameters, false alarm rate, detection precision, detection sensitivity, and prediction overhead. The performance analysis showed that our UAV-IDS-ConvNet system outperforms several recent existing intrusion detection systems developed to secure the UAV communication networks by (6–23) %.
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References
Yamada W, Yamada K, Manabe H, Ikeda D (2017) iSphere: self-luminous spherical drone displays. In: Proceedings of the 30th annual ACM symposium on user interface software and technology
Innocente MS, Grasso P (2019) Self-organizing swarms of firefighting drones: harnessing the power of collective intelligence in decentralized multi-robot systems. J Comput Sci 34:80–101
Emmanouil B, Geroliminis N (2020) On the new era of urban traffic monitoring with massive drone data: the PNEUMA large-scale field experiment. Transp Res Part C Emerg Technol 111:50–71
Hii MSY, Courtney P, Royall PG (2019) An evaluation of the delivery of medicines using drones. Drones 3(3):52
Kulbacki M, Segen J, Knieć W, Klempous R, Kluwak K, Nikodem J, Kulbacka J, Serester A (2018) Survey of drones for agriculture automation from planting to harvest. In: 2018 IEEE 22nd international conference on intelligent engineering systems (INES). IEEE
Bassoli R, Sacchi C, Granelli F, Ashkenazi I (2019) A virtualized border control system based on UAVs: design and energy efficiency considerations. In: IEEE aerospace conference. IEEE
Vahid B (2017) Cyber-physical attacks on UAS networks-challenges and open research problems. Preprint http://arxiv.org/abs/1702.01251
Elmarie B, Cloete E, Venter LM (2001) A comparison of intrusion detection systems. Comput Secur 20(8):676–683
Shakhatreh H, Sawalmeh AH, Al-Fuqaha A, Dou Z, Almaita E, Khalil I, Othman NS, Khreishah A, Guizani M (2019) Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 7:48572–48634
Animesh P, Park JM (2007) An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput Netw 51(12):3448–3470
Li D, Yu D (2014) Deep learning: methods and applications. Found Trends Signal Process 7(3–4):197–387
Satam P, Hariri S (2021) WIDS: an Anomaly based intrusion detection system for Wi-Fi (IEEE 802.11) protocol. IEEE Trans Netw Serv Manag 18(1):1077–1091
Abu Al-Haija Q, CD McCurry, Zein-Sabatto S (2021) Intelligent Self-reliant cyber-attacks detection and classification system for IoT communication using deep convolutional neural network. In: The 12th international networking conference. INC 2020. Lecture notes in networks and systems, vol 180, Springer, Cham
Zhao L, Alipour-Fanid A, Slawski M, Zeng K (2018) Prediction-time efficient classification using feature computational dependencies. In: Proceedings of the 24th ACM SIGKDD conference on knowledge discovery and data mining (KDD 2018), Research track, London, United Kingdom, pp 2787–2796
Choudhary G, Sharma V, You I, Yim K, Chen R, Cho JH (2018) Intrusion detection systems for networked unmanned aerial vehicles: a survey. In: 14th international wireless communications & mobile computing conference (IWCMC). Limassol, pp. 560–565. https://doi.org/10.1109/IWCMC.2018.8450305
Taher KA, Jisan BMY, Rahman MM (2019) network intrusion detection using supervised machine learning technique with feature selection. In: Proceedings of the international conference on robotics, electrical and signal processing techniques (ICREST), Bangladesh, South Asia, pp 643–646
Gao X, Shan C, Hu C, Niu Z, Liu Z (2019) An Adaptive ensemble machine learning model for intrusion detection. IEEE Access 7:82512–82521
Sapre S, Ahmadi P, Islam K (2019) A robust comparison of the KDDCup99 and NSL-KDD IoT network intrusion detection datasets through various machine learning algorithms. Preprint http://arxiv.org/abs:1912.13204v1
Chowdhury MMU, Hammond F, Konowicz G, Xin C, Wu H, Li J (2017) A few-shot deep learning approach for improved intrusion detection. In: Proceedings of the 2017 IEEE 8th annual ubiquitous computing, Electronics and mobile communication conference (UEMCON), New York, pp 456–462
Al-Haija QA, Zein-Sabatto S (2020) An efficient deep-learning-based detection and classification system for cyber-attacks in IoT communication networks. Electron MDPI 9:2152
Hoang TM, Nguyen NM, Duong TQ (2020) Detection of eavesdropping attack in UAV-aided wireless systems: unsupervised learning with one-class SVM and K-means clustering. IEEE Wirel Commun Lett 9(2):139–142
Bithas PS, Michailidis ET, Nomikos N, Vouyioukas D, Kanatas AG (2019) A survey on machine-learning techniques for UAV-based communications. Sens MDPI 19(23):5170
Riahi MM, Kenney J, Hu WC, Devabhaktuni VK, Kaabouch N (2019) Detection of GPS spoofing attacks on unmanned aerial systems. In: 16th IEEE annual consumer communications & networking conference (CCNC)
Xiao L, Lu X, Xu D, Tang Y, Wang L, Zhuang W (2018) UAV relay in VANETs against smart jamming with reinforcement learning. IEEE Trans Veh Technol 67(5):4087–4097
Wang A, Wang W, Zhou H, Zhang J (2021) Network intrusion detection algorithm combined with group convolution network and snapshot ensemble. Symmetry 13(10):1814
Devan P, Khare N (2020) An efficient XGBoost–DNN-based classification model for network intrusion detection system. Neural Comput Appl 1–16
Wang B, Wang Z, Liu L, Liu D, Peng X (2019). Data-driven anomaly detection for UAV sensor data based on deep learning prediction model. In: 2019 Prognostics and system health management conference (PHM-Paris), IEEE, pp 286–290
Ivanov LI, Obukhova NA, Baranov PS (2020) Review of modern UAV detection algorithms using methods of computer vision. In: IEEE conference of russian young researchers in electrical and electronic engineering (EIConRus), pp 322–325
Rajadurai H, Gandhi UD (2020) A stacked ensemble learning model for intrusion detection in wireless network. Neural comput appl 32:1–9
Tao J, Han T, Li R (2021) Deep-Reinforcement-learning-based intrusion detection in aerial computing networks. IEEE Netw 35(4):66–72
Bhoi SK, Jena KK, Maniharika GV, Muduli S, Sahoo R, Bhol D (2019) Detection of intended and unintended misbehaviors in unmanned aerial vehicle network (UAVN). In: International conference on information technology (ICIT), Bhubaneswar, pp 222–227
Wang G, Hong H, Zhang Y, Wu J, Wang Y, Li S (2020) Realization of detection algorithms for key parts of unmanned aerial vehicle based on deep learning. In: International conference on wireless communications and signal processing (WCSP), pp 137–142
Niu W, Zhang X, Zhang X, Du X, Huang X, Guizani M (2020) Malware on internet of UAVs detection combining string matching and fourier transformation. IEEE Internet Things J 8:9905–9919
Manesh MR, Velashani MS, Ghribi, E, Kaabouch N (2019) Performance comparison of machine learning algorithms in detecting jamming attacks on ADS-B Devices. In: IEEE international conference on electro information technology (EIT), pp 200–206
Arthur MP (2019) Detecting signal spoofing and jamming attacks in UAV networks using a lightweight IDS. In: International conference on computer, information and telecommunication systems (CITS), pp 1–5
Tan X, Su S, Zuo Z, Guo X, Sun X (2019) Intrusion detection of UAVs based on the deep belief network optimized by PSO. Sens MDPI 19:5529
Sedjelmaci H, Senouci SM, Ansari N (2018) A Hierarchical detection and response system to enhance security against lethal cyber-attacks in UAV Networks. IEEE Trans Syst Man Cybern Syst 48(9):1594–1606
Sedjelmaci H, Senouci SM, Messous M (2016) How to detect cyber-attacks in unmanned aerial vehicles network. In: IEEE global communications conference (GLOBECOM), pp. 1–6
Luo R, Tian F, Qin T, Chen E, Liu TY (2018) Neural architecture optimization. In: Proceedings of the 32nd international conference on neural information processing systems (NIPS'18). Curran Associates Inc., Red Hook, NY, USA, pp 7827–7838
Idrissi MAJ, Ramchoun H, Ghanou Y, Ettaouil M (2016) Genetic algorithm for neural network architecture optimization. In: 2016 3rd International conference on logistics operations management (GOL), pp 1–4. https://doi.org/10.1109/GOL.2016.7731699
Parisi GI, Kemker R, Part JL, Kanan C, Wermter S (2019) Continual lifelong learning with neural networks: a review. Neural Netw 113:54–71
Fernández-Caramés TM, Blanco-Novoa O, Suárez-Albela M, Fraga-Lamas P (2018) A uav and blockchain-based system for industry 4.0 inventory and traceability applications. In: Multidisciplinary digital publishing institute proceedings, vol 4, no 1, p 26
Aggarwal S, Kumar N, Tanwar S (2021) Blockchain-envisioned UAV communication using 6G networks: open issues, use cases, and future directions. IEEE Internet Things J 8(7):5416–5441. https://doi.org/10.1109/JIOT.2020.3020819
Kolias C, Kambourakis G, Gritzalis S (2013) Attacks and countermeasures on 802.16: analysis assessment. IEEE Commun Surv Tuts 15:487–514
CICIDS Dataset. DS-0917: Intrusion Detection Evaluation Dataset. Available online: https://www.impactcybertrust.org/datasetview?idDataset=917. Accessed 2 Feb 2020
DDoS Dataset. Distributed Denial of Service (DDoS) attack Evaluation Dataset. Available online: https://www.unb.ca/cic/datasets/ddos-2019.html Accessed 2 Feb 2020
Moustafa N, Slay J (2015) UNSW-NB15: a comprehensive data set for network intrusion detection systems UNSW-NB15 network data set). In: Proceedings of the 2015 military communications and information systems conference (MilCIS), Canberra, ACT, pp 1–6
Al-Haija QA, Smadi M, Zein-Sabatto S (2020) Multi-Class weather classification using ResNet-18 CNN for autonomous IoT and CPS applications. In: Proceeding of IEEE 7th annual conference on computational science & computational intelligence (CSCI'20), Las Vegas
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Abu Al-Haija, Q., Al Badawi, A. High-performance intrusion detection system for networked UAVs via deep learning. Neural Comput & Applic 34, 10885–10900 (2022). https://doi.org/10.1007/s00521-022-07015-9
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DOI: https://doi.org/10.1007/s00521-022-07015-9