Article: Intelligent cyber-attack detection in autonomous vehicles using residual network based deep learning model Journal: International Journal of Information and Computer Security (IJICS) 2026 Vol.29 No.4 pp.487 - 512 Abstract: Research on autonomous vehicles (AVs) has made significant progress in enhancing cybersecurity through intrusion detection system (IDS). The major contribution of the proposed study lies in addressing the demerits of existing models that frequently suffer from limited feature significance, high dimensional data, and poor evaluation of critical attacks. The proposed technique involves a robust vulnerability assessment method that utilises optimal feature selection and efficient classification algorithms. The binary mutation-based coati optimisation algorithm (BMCOA) is employed for dimensionality reduction by selecting the optimal feature subset. Additionally, a depth wise separable residual network with a bidirectional long short-term memory (DSResNet-Bi-LSTM) is introduced for the classification and detection of cyber-attacks. The performance of the IDS is evaluated using two datasets, namely the car hacking and SWaT datasets. The results demonstrate that the DSResNet-Bi-LSTM model outperforms existing techniques with an accuracy of 99.48%, precision of 99.2%, recall of 98.59%, and an F1-score of 98.02%. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent cyber-attack detection in autonomous vehicles using residual network based deep learning model

Authors: Masira M.S. Kulkarni; Prashant Dhotre; Mohd Shafi Pathan

Addresses: Department of CSE, MIT School of Computing, MIT ADT University, Loni Kalbhor, Pune, Maharashtra-412201, India ' Department of CSE, MIT School of Computing, MIT ADT University, Loni Kalbhor, Pune, Maharashtra-412201, India ' Department of CSE, MIT School of Computing, MIT ADT University, Loni Kalbhor, Pune, Maharashtra-412201, India

Abstract: Research on autonomous vehicles (AVs) has made significant progress in enhancing cybersecurity through intrusion detection system (IDS). The major contribution of the proposed study lies in addressing the demerits of existing models that frequently suffer from limited feature significance, high dimensional data, and poor evaluation of critical attacks. The proposed technique involves a robust vulnerability assessment method that utilises optimal feature selection and efficient classification algorithms. The binary mutation-based coati optimisation algorithm (BMCOA) is employed for dimensionality reduction by selecting the optimal feature subset. Additionally, a depth wise separable residual network with a bidirectional long short-term memory (DSResNet-Bi-LSTM) is introduced for the classification and detection of cyber-attacks. The performance of the IDS is evaluated using two datasets, namely the car hacking and SWaT datasets. The results demonstrate that the DSResNet-Bi-LSTM model outperforms existing techniques with an accuracy of 99.48%, precision of 99.2%, recall of 98.59%, and an F1-score of 98.02%.

Keywords: autonomous vehicles; intrusion detection systems; IDS; binary mutation-based coati optimisation algorithm; cyber-attacks and car hacking dataset.

DOI: 10.1504/IJICS.2026.153347

International Journal of Information and Computer Security, 2026 Vol.29 No.4, pp.487 - 512

Received: 14 Dec 2024
Accepted: 15 Nov 2025
Published online: 01 May 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Intelligent cyber-attack detection in autonomous vehicles using residual network based deep learning model

Keep up-to-date