Autoencoder and Decoder for Efficient Network Intrusion Detection in Internet of Vehicles

The Internet of Vehicles (IoV) is transforming transportation connectivity worldwide by connecting infrastructure, vehicles and communication technologies. While this real-time connectivity increases productivity, safety and security, it also poses serious security risks due to unauthorized access and criminal activity because it relies on legacy systems is not detected by traditional signature-based detection systems new threats May not be detected can catch it. This study compares an auto-encoder-based intrusion detection system designed to address the unique challenges of the Internet of Vehicles (I oV ) with traditional supervised methods such as SVM and unsupervised methods such as single-class SVM and K-means clustering, and LSTM. With the help of its decoder, the autoencoder improves interpretability by precisely reconstructing the input data, identifying abnormalities, and visualizing them. It trains on typical I oV network traffic data. Reducing false positives and increasing detection accuracy, this unsupervised process doesn't require a large amount of labelled data. In order to enable efficient and proactive defense against new cyber threats, the article uses this comparison to show how superior autoencoders are at guaranteeing high security and dependability for Internet of Vehicles ( I oV) systems.