Flow-based intrusion detection system in Vehicular Ad hoc Network using context-aware feature extraction

Abstract

Autonomous transportation systems have an immense impact on the way of our day-to-day commute or traveling. Connectedness is an inseparable part of these systems, and it is possible through Intelligent Transportation Systems applications. These applications use a network system known as Vehicular Ad hoc Network (VANET). However, the safety that is provided by VANETs can be easily compromised by malicious users. Hence there is a need for an Intrusion Detection System (IDS). We designed an IDS model that can collect network data cooperatively from vehicles and Roadside Units (RSUs). For training the core of our proposed IDS we generated synthetic network data with the help of the widely used Network Simulator 3 (ns-3) and Simulation of Urban Mobility (SUMO) simulation tools. We also generated separate test data that is not just a split part of the training data. This ensures the elimination of the misleading performance result of an overfitted model. We employed a multi-class IDS using Convolutional Neural Network (CNN) with a novel feature extraction method known as Context-Aware Feature Extraction-Based CNN (CAFECNN). The CAFECNN model takes advantage of the collected network flow data to detect both passive and active types of attacks. The results show that the proposed model is stronger in identification of hard-to-detect passive attacks compared to traditional machine learning methods. We also evaluated the model using real-time simulations and observed an immediate improvement in the performance of the network despite the intruders, especially in terms of packet delivery ratio and network throughput.