Enhanced Smart Vehicle Intrusion Detection Using Random Forest Classifier
Keywords:
Random Forest, Gradient Boosting, Adaboost, LSTM, CatBoost classifiersAbstract
This research introduces a newly developed Intrusion Detection System (IDS) for smart vehicles that leverages sophisticated machine learning techniques. The system has been engineered to identify and categorize various cybersecurity threats, including Distributed Denial of Service (DDoS) attacks, Fuzzy attacks, and Impersonation attacks, while also recognizing normal "Free" traffic patterns. For training and testing the models, researchers utilized the CAN-intrusion-dataset, which incorporates essential vehicle communication elements such as Message_ID, signal data at the byte level, and corresponding Target classifications. The investigation implements several machine learning approaches—Random Forest, Gradient Boosting, Adaboost, LSTM, and CatBoost classifiers—to detect and counter potential security threats. By harnessing these advanced algorithms, the system strives to deliver dependable and instantaneous detection of suspicious activities within vehicle networks, thereby strengthening the security posture and operational dependability of smart vehicle technologies. The ultimate goal is to develop an efficient and scalable IDS capable of protecting smart vehicles from evolving cyber threats.
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