Jul 2026· International Conference on Computer, Information and Telecommunication Systems· pp. 1-8· 0 citations· 47 references
Abstract
Critical safety functions in modern vehicles rely heavily on intra-vehicle networks (IVNs), primarily via the Controller Area Network (CAN) protocol. The inherent vulnerabilities of CAN require robust intrusion detection systems (IDS) to mitigate adversarial threats. However, state-of-the-art IDS, especially AI-based approaches, often lack a comprehensive, well-defined performance analysis method. This work proposes and evaluates a structured pipeline for in-vehicle IDS, analyzing an autoencoder semi-supervised IDS as a practical case study. The method is validated on publicly available datasets, covering multiple attack types, with additional analysis of generalization capabilities. Performance is rigorously assessed using precision, recall, F1-score, and the Matthews Correlation Coefficient (MCC), chosen for its robustness in imbalanced scenarios. Results demonstrated highly efficient identification of DoS attacks (MCC 1.00), though Fuzzy DoS detection showed lower performance (MCC 0.214 in CAN-MIRGU and 0.074 in CAN-MODES). These findings support the viability of the proposed pipeline for IDS analysis focusing on enhancing CAN network security, consistent with recent research trends.
It was found that hybrid and ensemble models provide the highest accuracy in complex environments (over 99%), while the combination of convolutional neural networks with recurrent networks or Transformers is the most effective solution for detecting multi-stage attacks.
V. Chelak, Oleksii Hornostal, Yehor Chelak et al.· Advanced Information Systems· 0 citations
Modern vehicles rely heavily on in-vehicle Controller Area Network (CAN) communication to coordinate safety-critical electronic control units (ECUs). However, the CAN protocol was not originally designed with authentication, encryption, or message integrity mechanisms, making it vulnerable to injection, spoofing, fuzzy...
Younis A. M. Al Shojaa, Redhwan M. A. Saad, Khaled A. M. Al Soufy· 2026 6th International Confe...· 0 citations
In the era of contemporary data traffic routing, the concept of Intrusion Detection Systems (IDS) is substantially utilized. However, the efficacy of IDS is often decreased because of the reality that high-concentration traffic postfixes, sophisticated cyber criminals, and more and more stringent demands are tending to...
Krishna Kumar Tiwari· 2026 International Conferenc...· 0 citations
The study analyzes the most recent progress in ML and DL methods used to develop IDS that operate in WSNs through analysis of their primary algorithms and algorithmic combinations and concludes that the DL and hybrid approaches are superior to conventional ML algorithms in handling complicated and imbalanced datasets.
Priyanka Sharma, Mohd. Suhaib Kidwai, Piyush Charan· International journal of com...· 0 citations
The experimental results show that the hybrid deep learning architecture outperforms individual models in network intrusion detection, and the hybrid model combining CNN, LSTM, and GRU achieved near-perfect accuracy, with extremely low false-positive and false-negative rates.
S. S. Goje, S. Asutkar, G. Asutkar· African Journal Of Applied R...· 0 citations
This work proposed a modular weight-based framework that evaluates cross-layer Machine Learning (ML) IDS across multiple dimensions, namely, detection effectiveness and generalizability, data quality, and attack coverage and practical deployability, and applied this framework to the state-of-the-art cross-layer ML IDS...
Dimitrios Tasiopoulos, A. Xenakis, A. Lekidis et al.· Electronics· 0 citations
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