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Anomaly Detection in the Internet of Vehicular Networks Using Explainable Neural Networks (xNN)

Author

Listed:
  • Saddam Aziz

    (Centre for Advances in Reliability and Safety, New Territories, Hong Kong)

  • Muhammad Talib Faiz

    (Centre for Advances in Reliability and Safety, New Territories, Hong Kong)

  • Adegoke Muideen Adeniyi

    (Centre for Advances in Reliability and Safety, New Territories, Hong Kong)

  • Ka-Hong Loo

    (Centre for Advances in Reliability and Safety, New Territories, Hong Kong
    Department of Electronic and Information Engineering, The Hong Kong Polytechnic University (PolyU), Hung Hom, Hong Kong)

  • Kazi Nazmul Hasan

    (School of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3000, Australia)

  • Linli Xu

    (Department of Electronic and Information Engineering, The Hong Kong Polytechnic University (PolyU), Hung Hom, Hong Kong)

  • Muhammad Irshad

    (Department of Electronic and Information Engineering, The Hong Kong Polytechnic University (PolyU), Hung Hom, Hong Kong)

Abstract

It is increasingly difficult to identify complex cyberattacks in a wide range of industries, such as the Internet of Vehicles (IoV). The IoV is a network of vehicles that consists of sensors, actuators, network layers, and communication systems between vehicles. Communication plays an important role as an essential part of the IoV. Vehicles in a network share and deliver information based on several protocols. Due to wireless communication between vehicles, the whole network can be sensitive towards cyber-attacks.In these attacks, sensitive information can be shared with a malicious network or a bogus user, resulting in malicious attacks on the IoV. For the last few years, detecting attacks in the IoV has been a challenging task. It is becoming increasingly difficult for traditional Intrusion Detection Systems (IDS) to detect these newer, more sophisticated attacks, which employ unusual patterns. Attackers disguise themselves as typical users to evade detection. These problems can be solved using deep learning. Many machine-learning and deep-learning (DL) models have been implemented to detect malicious attacks; however, feature selection remains a core issue. Through the use of training empirical data, DL independently defines intrusion features. We built a DL-based intrusion model that focuses on Denial of Service (DoS) assaults in particular. We used K-Means clustering for feature scoring and ranking. After extracting the best features for anomaly detection, we applied a novel model, i.e., an Explainable Neural Network (xNN), to classify attacks in the CICIDS2019 dataset and UNSW-NB15 dataset separately. The model performed well regarding the precision, recall, F1 score, and accuracy. Comparatively, it can be seen that our proposed model xNN performed well after the feature-scoring technique. In dataset 1 (UNSW-NB15), xNN performed well, with the highest accuracy of 99.7%, while CNN scored 87%, LSTM scored 90%, and the Deep Neural Network (DNN) scored 92%. xNN achieved the highest accuracy of 99.3% while classifying attacks in the second dataset (CICIDS2019); the Convolutional Neural Network (CNN) achieved 87%, Long Short-Term Memory (LSTM) achieved 89%, and the DNN achieved 82%. The suggested solution outperformed the existing systems in terms of the detection and classification accuracy.

Suggested Citation

  • Saddam Aziz & Muhammad Talib Faiz & Adegoke Muideen Adeniyi & Ka-Hong Loo & Kazi Nazmul Hasan & Linli Xu & Muhammad Irshad, 2022. "Anomaly Detection in the Internet of Vehicular Networks Using Explainable Neural Networks (xNN)," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1267-:d:791499
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    References listed on IDEAS

    as
    1. E. Laxmi Lydia & A. Arokiaraj Jovith & A. Francis Saviour Devaraj & Changho Seo & Gyanendra Prasad Joshi, 2021. "Green Energy Efficient Routing with Deep Learning Based Anomaly Detection for Internet of Things (IoT) Communications," Mathematics, MDPI, vol. 9(5), pages 1-18, March.
    2. Jiang, Ying & Zhang, Junyi, 2019. "Interaction between company Manager's and Driver's decisions on expressway routes for truck transport," Transport Policy, Elsevier, vol. 76(C), pages 1-12.
    3. Rongquan Zhang & Saddam Aziz & Muhammad Umar Farooq & Kazi Nazmul Hasan & Nabil Mohammed & Sadiq Ahmad & Nisrine Ibadah, 2021. "A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor," Energies, MDPI, vol. 14(11), pages 1-22, May.
    4. Li, Yunfeng & Xue, Wenli & Wu, Ting & Wang, Huaizhi & Zhou, Bin & Aziz, Saddam & He, Yang, 2021. "Intrusion detection of cyber physical energy system based on multivariate ensemble classification," Energy, Elsevier, vol. 218(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Snezhana Gocheva-Ilieva & Atanas Ivanov & Hristina Kulina, 2023. "Special Issue “Statistical Data Modeling and Machine Learning with Applications II”," Mathematics, MDPI, vol. 11(12), pages 1-4, June.
    2. Furkat Safarov & Mainak Basak & Rashid Nasimov & Akmalbek Abdusalomov & Young Im Cho, 2023. "Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection," Future Internet, MDPI, vol. 15(9), pages 1-19, September.

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    More about this item

    Keywords

    IoV; xNN; K-MEANS; anomaly detection;
    All these keywords.

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