Jul 2026· Annual International Computer Software and Applications Conference· pp. 3274-3275· 0 citations· 4 references
Computer Science
Abstract
Controller Area Network (CAN) protocol, invented by Bosch in the 1980s and still fundamental today, is the backbone to facilitate communication between electronic systems. And modern day self-driving cars rely heavily on CAN for internal communications between their Electronic Control Units (ECUs). However, this protocol lacks intrinsic security measures, making In-Vehicle Networks (IVNs) susceptible to malicious attacks. This study demonstrates an AI-driven Intrusion Detection System (IDS) to identify anomalies in CAN bus traffic using a comparative suite of machine learning and deep learning models. Six models were systematically implemented and evaluated: Isolation Forest, a Dense Autoencoder, a Long Short-Term Memory (LSTM) Autoencoder, a Gated Recurrent Unit (GRU) Autoencoder, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) hybrid, and XGBoost. The CNN-LSTM and XGBoost achieved the highest detection accuracy at 89%, with F1-score of 0.89, demonstrating the feasibility of AI-based IDS solutions for enhancing real-time cybersecurity posture of modern connected vehicles.
A hybrid deep-learning intrusion detection system (IDS) that combines one-dimensional convolutional layers, a bidirectional long short-term memory (BiLSTM) network, and a temporal attention mechanism to detect malicious activity directly from CAN frame streams is presented.
Ginne M James· International Journal of Inf...· 0 citations
LSTM had good detection for frequent attacks and slow-changing patterns, which shows its capacity in learning long-lasting dependencies, which shows its capacity in learning long-lasting dependencies.
Jawad Hussain Awan, Misbah Safdar, Muhammad Ayaz Shirazi et al.· Italian National Conference...· 0 citations
Controller Area Network (CAN) is the dominant in-vehicle bus, yet its broadcast design and absent authentication leave it exposed to injection and spoofing attacks. Existing deep-learning intrusion detection systems achieve strong accuracy but depend on ML inference frameworks incompatible with the resource budgets of...
Amirmasoud Pourmiri, Ali Eslami, Sergio A. Salinas Monroy· International Conference on...· 0 citations
A taxonomy of IDS architectures and a survey of recently proposed networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, GRUs, Autoencoders, GANs, Transformer-based, as well as hybrids are developed.
Sajith K. V., Gripsy Paul, Bhagavant Deshpande et al.· Journal of Network and Infor...· 0 citations
Investigation of deep learning models for binary network intrusion detection using the NSL-KDD benchmark dataset indicates that carefully designed standalone architectures can match or exceed the performance of more complex hybrid and ensemble models for binary intrusion detection, while incurring substantially lower c...
Ketki Naik, Sanjeev Ghosh· International Journal for Re...· 0 citations
These findings confirm that a combination of spatial feature extraction, temporal sequence modeling, and imbalance-aware optimization not only ensures but also delivers a highly effective and more reliable intrusion detection solution for in-vehicle CAN network security.
A. Malini, Gerardine Immaculate Mary· PLoS ONE· 0 citations
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