A Mathematical Framework for Enhancing IOT Security in VANETs: Optimizing Intrusion Detection Systems through Machine Learning Algorithms

Abstract

Vehicular Ad Hoc Networks (VANETs) are of paramount importance to enable secure transportation, a requirement in smart city concepts because security threats can have catastrophic consequences on road safety. To mitigate this issue, authors supervised an efficient mathematical approach in form of IDS with a set of machine-learning algorithms for effective intrusion detection mechanism which secures the VANET environment especially when it comes to IoT security. To improve the accuracy and efficiency of intrusion detection a system is proposed that combines intelligence optimization algorithm such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization, along with Support Vector Machine (SVM) based Intrusion Detection System (IDS).The system will be assessed through the NSL-KDD dataset — a popular intrusion detection dataset that contains realistic network traffic data. This paper will benchmark the performance of three optimization algorithms based on their capabilities to optimize the accuracy of Support Vector Machines (SVM) classifier in attack types detection, including Denial-of-Service (DoS), Probing, Unauthorized Access via Remote to Local System Administrator privilege (U2R), and Unauthorized access from a remote machine (R2L). This holistic view attempts to establish an IDS that is more robust and dynamic in its architecture, such that it can effectively detect security threats while providing solutions within VANETs promoting IoT security for smart transportation.