An Adversarial Machine Learning-Based Fast Detection Method for Denial of Service-Oriented Cyber Attacks in Internet of Vehicles

Abstract

Denial of Service (DoS)-Oriented cyber attack has been a major threat for physical security in many kinds of network media, including the Internet of Vehicles (IoV). This paper focuses on the scenario of IoV, and proposes a machine learning-based fast detection method for adversarial neural network-based fast detection method for DoS-oriented cyber attacks. First, by analyzing the implementation principles and attack characteristics of three attack types, three aspects of statistical features are extracted: maximum matching packet growth rate, source address entropy value, and flow table similarity. Then, they are used as the input features to establish an adversarial machine learning-based DoS cyber attack detection method. On this basis, the field features of six stream rules are extracted, and two DoS cyber attack detection methods via machine learning are formulated. The proposals are able to detect the low-rate DoS-based cyber attacks against the data layer. The experimental results show that the proposed DoS attack detection method based on machine learning can effectively detect three DoS attacks under IoV, and these two algorithms have higher detection rates when compared with other algorithms.