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In modern automotive systems, introducing multiple connectivity protocols has transformed in-vehicle network communication, resulting in the widely recognized Controller Area Network (CAN) standard. Despite its ubiquitous use, the CAN protocol lacks critical security features, making vehicle communications vulnerable to message injection attacks. These assaults might confuse original electronic control units (ECUs) or cause system failures, emphasizing the need for strong cybersecurity solutions in automobile networks. This study addresses this need by developing a quick and efficient abnormal traffic detection system to protect vehicular communications from cyber attacks. The proposed system utilizes four machine learning techniques: Adaboost Trees (ABT), Coarse Decision Trees (CDT), Naive Bayes Classifier (NBC), and Support Vector Machine (SVM). These models were carefully assessed on the Car-Hacking-2018 dataset, which simulates real-time vehicular communication scenarios. Specifically, the system considers five balanced classes, including one normal traffic class and four classes for message injection attacks over the in-vehicle controller area network: fuzzy attack, DoS attack, RPM attack (spoofing), and gear attack (spoofing). Our best performance outcomes belong to the ABT model, which notched 99.8% classification accuracy and 6.67 µs of classification overhead. Such results have outweighed existing in-vehicle intrusion detection systems employing the same/similar dataset.
Q. Abu Al-Haija, Analytical study for cyber dynamic continuous time vehicle model with networked PID controller, Math. Model. Eng. Probl., vol. 9, no. 6, pp. 1685–1692, 2022.
M. Bozdal, M. Samie, S. Aslam, and I. Jennions, Evaluation of CAN bus security challenges, Sensors, vol. 20, no. 8, p. 2364, 2020.
S. F. Lokman, A. T. Othman, and M. H. Abu-Bakar, Intrusion detection system for automotive controller area network (CAN) bus system: a review, EURASIP J. Wirel. Commun. Netw., vol. 2019, no. 1, p. 184, 2019.
Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, Network intrusion detection system: A systematic study of machine learning and deep learning approaches, Trans. Emerg. Telecommun. Technol., vol. 32, no. 1, p. e4150, 2021.
D. Li, L. Deng, M. Lee, and H. Wang, IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning, Int. J. Inf. Manag., vol. 49, pp. 533–545, 2019.
E. Anthi, L. Williams, M. Slowinska, G. Theodorakopoulos, and P. Burnap, A supervised intrusion detection system for smart home IoT devices, IEEE Internet Things J., vol. 6, no. 5, pp. 9042–9053, 2019.
H. Liu and B. Lang, Machine learning and deep learning methods for intrusion detection systems: A survey, Appl. Sci., vol. 9, no. 20, p. 4396, 2019.
Q. Abu Al-Haija and M. Krichen, A lightweight in-vehicle alcohol detection using smart sensing and supervised learning, Computers, vol. 11, no. 8, p. 121, 2022.
Z. El-Rewini, K. Sadatsharan, D. F. Selvaraj, S. J. Plathottam, and P. Ranganathan, Cybersecurity challenges in vehicular communications, Veh. Commun., vol. 23, p. 100214, 2020.
Y. Ai, F. A. P. de Figueiredo, L. Kong, M. Cheffena, S. Chatzinotas, and B. Ottersten, Secure vehicular communications through reconfigurable intelligent surfaces, IEEE Trans. Veh. Technol., vol. 70, no. 7, pp. 7272–7276, 2021.
A. Lei, Y. Cao, S. Bao, D. Li, P. Asuquo, H. Cruickshank, and Z. Sun, A blockchain based certificate revocation scheme for vehicular communication systems, Future Gener. Comput. Syst., vol. 110, pp. 892–903, 2020.
T. Zeng, O. Semiari, M. Chen, W. Saad, and M. Bennis, Federated learning on the road autonomous controller design for connected and autonomous vehicles, IEEE Trans. Wireless Commun., vol. 21, no. 12, pp. 10407–10423, 2022.
A. A. Alsulami, Q. Abu Al-Haija, A. Alqahtani, and R. Alsini, Symmetrical simulation scheme for anomaly detection in atonomous vhicles bsed on LSTM mdel, Symmetry, vol. 14, no. 7, p. 1450, 2022.
A. Zhou, Z. Li, and Y. Shen, Anomaly detection of CAN bus messages using a deep neural network for autonomous vehicles, Appl. Sci., vol. 9, no. 15, p. 3174, 2019.
W. Lo, H. Alqahtani, K. Thakur, A. Almadhor, S. Chander, and G. Kumar, A hybrid deep learning based intrusion detection system using spatial-temporal representation of in-vehicle network traffic, Veh. Commun., vol. 35, p. 100471, 2022.
Q. Abu Al-Haija and A. A. Alsulami, Detection of fake replay attack signals on remote keyless controlled vehicles using pre-trained deep neural network, Electronics, vol. 11, no. 20, p. 3376, 2022.
T. H. H. Aldhyani and H. Alkahtani, Attacks to automatous vehicles: A deep learning algorithm for cybersecurity, Sensors, vol. 22, no. 1, p. 360, 2022.
A. R. Javed, S. U. Rehman, M. U. Khan, M. Alazab, and G. Thippa Reddy, CANintelliIDS: detecting in-vehicle intrusion attacks on a controller area network using CNN and attention-based GRU, IEEE Trans. Netw. Sci. Eng., vol. 8, no. 2, pp. 1456–1466, 2021.
A. A. Alsulami, Q. Abu Al-Haija, B. Alturki, A. Alqahtani, and R. Alsini, Security strategy for autonomous vehicle cyber-physical systems using transfer learning, J. Cloud Comput., vol. 12, no. 1, p. 181, 2023.
N. Kabilan, V. Ravi, and V. Sowmya, Unsupervised intrusion detection system for in-vehicle communication networks, J. Saf. Sci. Resil., vol. 5, no. 2, pp. 119–129, 2024.
M. H. Khan, A. R. Javed, Z. Iqbal, M. Asim, and A. I. Awad, DivaCAN: Detecting in-vehicle intrusion attacks on a controller area network using ensemble learning, Comput. Secur., vol. 139, p. 103712, 2024.
Q. Abu Al-Haija and A. Al-Tamimi, Secure aviation control through a streamlined ADS-B perception system, Appl. Syst. Innov., vol. 7, no. 2, p. 27, 2024.
H. M. Song, J. Woo, and H. K. Kim, In-vehicle network intrusion detection using deep convolutional neural network, Veh. Commun., vol. 21, p. 100198, 2020.
X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, A survey on ensemble learning, Front. Comput. Sci., vol. 14, no. 2, pp. 241–258, 2020.
F. Wang, Z. Li, F. He, R. Wang, W. Yu, and F. Nie, Feature learning viewpoint of adaboost and a new algorithm, IEEE Access, vol. 7, pp. 149890–149899, 2019.
Q. Abu Al-Haija, A. Smadi, and M. Allehyani, Meticulously intelligent identification system for smart grid network stability to optimize risk management, Energies, vol. 14, no. 21, p. 6935, 2021.
A. A. Alsulami, Q. Abu Al-Haija, A. Tayeb, and A. Alqahtani, An intrusion detection and classification system for IoT traffic with improved data engineering, Appl. Sci., vol. 12, no. 23, p. 12336, 2022.
N. Abd, K. M. Ali Alheeti, and S. S. Al-Rawi, Intelligent intrusion detection system in internal communication systems for driverless cars, Webology, vol. 17, no. 2, pp. 376–393, 2020.
F. Pascale, E. A. Adinolfi, S. Coppola, and E. Santonicola, Cybersecurity in automotive: An intrusion detection system in connected vehicles, Electronics, vol. 10, no. 15, p. 1765, 2021.
M. Deveci, D. Pamucar, I. Gokasar, M. Köppen, and B. B. Gupta, Personal mobility in metaverse with autonomous vehicles using Q-rung orthopair fuzzy sets based OPA-RAFSI model, IEEE Trans. Intell. Transp. Syst., vol. 24, no. 12, pp. 15642–15651, 2023.
I. Gokasar, D. Pamucar, M. Deveci, B. B. Gupta, L. Martinez, and O. Castillo, Metaverse integration alternatives of connected autonomous vehicles with self-powered sensors using fuzzy decision making model, Inf. Sci., vol. 642, p. 119192, 2023.
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