An Intelligent Intrusion Detection System in IoV Using Machine Learning and Deep Learning Models

ABSTRACT Creating an internet of vehicles (IoV) intelligent intrusion detection system (IDS) that can identify and stop a wide range of new and evolving cyberattacks is a challenging task. IoV generates vast amounts of data from sensors and communication protocols, necessitating an IDS capable of handling various data formats and extracting useful information. Real‐time attack identification and mitigation require efficient model inference with minimal latency. Cyberattackers employ sophisticated techniques, necessitating an IDS capable of learning and adapting to new threats. By oversampling the minority class with SMOTE, SMOTEBoost increases the training data available for learning its features, leading to better detection of minority class instances. The boosting component focuses on training weak learners on misclassified instances, further improving specificity toward the minority class and reducing false positives. The paper describes a way to choose important features for finding intrusions in the IoV that includes nonnegative latent factor dimensionality‐minimizing intraclass compactness (NLF‐DMIC). For classification, this paper proposes a four‐model decision tree (DT), a random forest (RF), an enhanced LSTM network with a conventional long‐short‐term memory. Tested on the CICIDS‐2018 and Car‐Hacking datasets, the suggested approach showed the best intrusion detection performance. The ILSTM outperformed various ML and DL approaches, achieving 98.68% accuracy with SMOTEBOOST on the CICIDS2018 dataset and 98.87% accuracy with SMOTEBOOST on the Car‐Hacking dataset. An intelligent IDS for IoV using machine learning (ML) and deep learning (DL) models enhances recognition accuracy, reduces false alarms, and adapts to dynamic threats, ensuring robust, real‐time security for connected vehicles.