MICORD-IDS: A Hybrid Learning System for Intrusion Detection System for the Internet of Vehicles

Recent advancements in cyber technologies have created a demand for smart vehicles. The network of these smart vehicles is often called as Internet of Vehicles (IoV). The swift increase in the IoV has led to security challenges which can lead to unwarranted and lethal accidents. The security of IoV is needed faster. Lightweight and accurate algorithms to stop any malicious attacks at the vehicle of IoV. This article proposes MICORD Intrusion Detection Systems (IDS). The proposed IDS system is based on the feature selection technique involving Mutual Information with the further reduction of features using Correlation and a Decision Tree classifier. The performance of the proposed system was tested using the CICIDS2018 dataset. The system secured an exceptionally good accuracy and an incredibly good F1 Score of 98.6% which is comparable to the latest available studies.