International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 3 May-June 2024 Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

Intelligent Intrusion Detection System in Internet of Vehicles using Random Forest, Logistic Regression and Decision Tree Algorithm

Author(s) Nelamalli Meghana Kiran, Sujith Kiran Nelamalli, Usha. G
Country India
Abstract This study introduces an Intelligent Intrusion Detection System (IDS) for Internet of Vehicles (IoV) security. Using Random Forest, Logistic Regression, and Decision Tree algorithms, it detects and prevents intrusions in real-time. By combining these algorithms, the IDS aims to improve accuracy and adapt to evolving threats. Evaluation metrics include accuracy (93.6%), precision (94.4%), recall (95.3%), and F1 score (96.7%). Analysis of false positives/negatives informs refinement for real-world use. The IDS enhances cybersecurity for connected vehicles. This IDS provides a robust defense against cyber threats in the IoV ecosystem, ensuring user safety and privacy. Its multi-algorithmic approach and high-performance metrics make it a reliable solution for safeguarding connected vehicles.
Keywords Intrusion Detection System Internet of Vehicles Random Forest Logistic Regression Decision Tree
Field Computer > Network / Security
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-26
Cite This Intelligent Intrusion Detection System in Internet of Vehicles using Random Forest, Logistic Regression and Decision Tree Algorithm - Nelamalli Meghana Kiran, Sujith Kiran Nelamalli, Usha. G - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.18748
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.18748
Short DOI https://doi.org/gtwmwc

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