Hybrid Approaches for Intrusion Prediction in IoV (Internet of Vehicles)
Authors : Naveen Joshi1, Dr Nirmal Kaur2
1Research Scholar, SBBSU, Jalandhar, naveenjoshi84@gmail.com
2 Associate Professor, SBBSU, Jalandhar
Abstract The Internet of Vehicles (IoV) is a burgeoning field integrating smart vehicles into a connected ecosystem, enabling Vehicle-to-Everything (V2X) communications. However, this connectivity increases vulnerability to cyber threats, necessitating robust intrusion detection systems (IDS). This paper explores hybrid approaches combining signature-based and anomaly-based detection methods to enhance security in IoV. We discuss the architecture, algorithms, and performance metrics of hybrid IDS, emphasizing their advantages and potential challenges. A detailed analysis of hybrid IDS implementations is presented, supported by diagrams and empirical data.
Keywords: Internet of Vehicles (IoV), Intrusion Detection Systems (IDS), Machine Learning (ML), Deep Learning (DL), Hybrid Approaches, Cyber security, Vehicle-to-Everything (V2X), Anomaly Detection, Predictive Security, Network Security.
DOI link – https://doi.org/10.69758/jfzi9997
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