This paper proposes a hybrid deep learning method with a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network coupled with a Random Forest classifier for intrusion detection in connected vehicles. The model was trained and evaluated on the DECIMAL dataset, a realistic in-vehicle network intrusion data set with Controller Area Network (CAN) bus traffic. The CNN-LSTM model is trained on spatial-temporal features from CAN messages, while the Random Forest classifier exploits these features for accurate cyberattack classification. Experimental results demonstrate the superior performance of the model with an average detection accuracy of 99.62% and good precision and recall of various attack types. The hybrid approach outperforms traditional standalone approaches by addressing primary challenges of automotive cybersecurity, such as identification of sophisticated temporal patterns and reduction of false alarms. This research stresses the need for state-of-the-art machine learning techniques in the security of networked vehicles, particularly the Internet of Vehicles (IoV) environment. The findings emphasize the requirement for hybridization of deep learning with ensemble methods in order to boost real-time threat detection and system robustness. Future work will focus on optimizing the model for embedded automotive hardware and exploring its generalizability across diverse datasets. This study contributes to the development of secure intelligent transportation systems through the provision of a robust framework for identification and the containment of cyber-attacks on networked vehicles.This paper proposes a hybrid deep learning method with a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network coupled with a Random Forest classifier for intrusion detection in connected vehicles. The model was trained and evaluated on the DECIMAL dataset, a realistic in-vehicle network intrusion data set with Controller Area Network (CAN) bus traffic. The CNN-LSTM model is trained on spatial-temporal features from CAN messages, while the Random Forest classifier exploits these features for accurate cyberattack classification. Experimental results demonstrate the superior performance of the model with an average detection accuracy of 99.62% and good precision and recall of various attack types. The hybrid approach outperforms traditional standalone approaches by addressing primary challenges of automotive cybersecurity, such as identification of sophisticated temporal patterns and reduction of false alarms. This research stresses the need for state-of-the-art machine learning techniques in the security of networked vehicles, particularly the Internet of Vehicles (IoV) environment. The findings emphasize the requirement for hybridization of deep learning with ensemble methods in order to boost real-time threat detection and system robustness. Future work will focus on optimizing the model for embedded automotive hardware and exploring its generalizability across diverse datasets. This study contributes to the development of secure intelligent transportation systems through the provision of a robust framework for identification and the containment of cyber-attacks on networked vehicles. Read More
Related posts:
Driver Pattern Recognition Using ML Quantization and Tabular Cloning Approach
An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron
Machine learning-based network anomaly detection: design, implementation, and evaluation
Noninvasive deep learning analysis for Smith-Magenis syndrome classification
Integrating CWT and CNN for damage detection in high-speed
railway bridges
GPS Data and Machine Learning Tools, a Practical and Cost-Effective Combination for Estimating Light Vehicle Emissions