A Mathematical Modeling of Stuxnet-Style Autonomous Vehicle Malware
Haesung Ahn, Juyeong Choi, Yong Hoon Kim
Abstract
Autonomous vehicles (AVs) have the potential to provide new paradigms to enhance the safety, mobility, and environmental sustainability of surface transportation. However, as vehicles become more computerized and internally interconnected by electronic control systems, their vulnerability to cyber-attacks is a fast-growing concern and a national priority. Evidence from the Internet virus suggests that AVs will have critical challenges posed by epidemic-style malware like Stuxnet. This self-propagating malware is a fast and powerful way of disrupting the AV system and transportation infrastructure. This study presents a mathematical model for Stuxnet-style malware’s temporal and spatial spread. Taking cues from the field of epidemiology and ecology, the malware will be described as an infectious epidemic to capture the dynamics of temporal and spatial propagation behavior. This study is the first attempt to analyze the spread of Stuxnet-style malware on AVs. The future uses of such a model for the temporal-geographic spread of AVs-based infectious malware are discussed.