Biblio

Recent years have seen an increase in the data usage in cars, particularly as they become more autonomous and connected. With the rise in data use have come concerns about automotive cyber-security. An in-vehicle network shown to be particularly vulnerable is the Controller Area Network (CAN), which is the communication bus used by the car's safety critical and performance critical components. Cyber attacks on the CAN have been demonstrated, leading to research to develop attack detection and attack prevention systems. Such research requires representative attack demonstrations and data for testing. Obtaining this data is problematical due to the expense, danger and impracticality of using real cars on roads or tracks for example attacks. Whilst CAN simulators are available, these tend to be configured for testing conformance and functionality, rather than analysing security and cyber vulnerability. We therefore adapt a leading, industry-standard, CAN simulator to incorporate a core set of cyber attacks that are representative of those proposed by other researchers. Our adaptation allows the user to configure the attacks, and can be added easily to the free version of the simulator. Here we describe the simulator and, after reviewing the attacks that have been demonstrated and discussing their commonalities, we outline the attacks that we have incorporated into the simulator.

As information systems become increasingly interdependent, there is an increased need to share cybersecurity data across government agencies and companies, and within and across industrial sectors. This sharing includes threat, vulnerability and incident reporting data, among other data. For cyberattacks that include sociotechnical vectors, such as phishing or watering hole attacks, this increased sharing could expose customer and employee personal data to increased privacy risk. In the US, privacy risk arises when the government voluntarily receives data from companies without meaningful consent from individuals, or without a lawful procedure that protects an individual's right to due process. In this paper, we describe a study to examine the trade-off between the need for potentially sensitive data, which we call incident data usage, and the perceived privacy risk of sharing that data with the government. The study is comprised of two parts: a data usage estimate built from a survey of 76 security professionals with mean eight years' experience; and a privacy risk estimate that measures privacy risk using an ordinal likelihood scale and nominal data types in factorial vignettes. The privacy risk estimate also factors in data purposes with different levels of societal benefit, including terrorism, imminent threat of death, economic harm, and loss of intellectual property. The results show which data types are high-usage, low-risk versus those that are low-usage, high-risk. We discuss the implications of these results and recommend future work to improve privacy when data must be shared despite the increased risk to privacy.