Performance Improvement of Machine Learning Algorithm using PCA on IoV Attack

Abstrak

In the transportation industry, the Internet of Vehicles (IoV) is an advancement of the Internet of Things (IoT), allowing automobiles to connect to networks to provide a range of features. This connectivity transforms traditional vehicles into intelligent systems, fostering innovations like autonomous driving and traffic optimization. However, this increased connectivity exposes IoV to cybersecurity threats, particularly because the networks utilized are often public and lack robust security measures. Cyberattacks targeting IoV can involve data packet modification, traffic flooding, or spoofing, potentially disabling critical vehicle components, compromising passenger safety, and increasing the risk of accidents. Consequently, accurate and efficient attack detection systems are essential to counter these threats and ensure IoV security. This study leverages the CICIoV2024 dataset and applies Principal Component Analysis (PCA) to enhance computational efficiency in detecting IoV attacks. The algorithms employed in this research include Random Forest, AdaBoost, Logistic Regression, and Deep Neural Networks. Experimental results demonstrate that implementing PCA significantly improves computational efficiency across all algorithms while maintaining consistent accuracy and F1-Score, highlighting its effectiveness in securing IoV systems.