Cyber-Physical Systems Security—A Survey <s> 4) Smart Cars Attacks: <s> Modern automobiles are pervasively computerized, and hence potentially vulnerable to attack. However, while previous research has shown that the internal networks within some modern cars are insecure, the associated threat model--requiring prior physical access--has justifiably been viewed as unrealistic. Thus, it remains an open question if automobiles can also be susceptible to remote compromise. Our work seeks to put this question to rest by systematically analyzing the external attack surface of a modern automobile. We discover that remote exploitation is feasible via a broad range of attack vectors (including mechanics tools, CD players, Bluetooth and cellular radio), and further, that wireless communications channels allow long distance vehicle control, location tracking, in-cabin audio exfiltration and theft. Finally, we discuss the structural characteristics of the automotive ecosystem that give rise to such problems and highlight the practical challenges in mitigating them. <s> BIB001 </s> Cyber-Physical Systems Security—A Survey <s> 4) Smart Cars Attacks: <s> Abstract The IT security of automotive systems is an evolving area of research. To analyse the current situation and the potentially growing tendency of arising threats we performed several practical tests on recent automotive technology. With a focus on automotive systems based on CAN bus technology, this article summarises the results of four selected tests performed on the control systems for the window lift, warning light and airbag control system as well as the central gateway. These results are supplemented in this article by a classification of these four attack scenarios using the established CERT taxonomy and an analysis of underlying security vulnerabilities, and especially, potential safety implications. With respect to the results of these tests, in this article we further discuss two selected countermeasures to address basic weaknesses exploited in our tests. These are adaptations of intrusion detection (discussing three exemplary detection patterns) and IT-forensic measures (proposing proactive measures based on a forensic model). This article discusses both looking at the four attack scenarios introduced before, covering their capabilities and restrictions. While these reactive approaches are short-term measures, which could already be added to today’s automotive IT architecture, long-term concepts also are shortly introduced, which are mainly preventive but will require a major redesign. Beneath a short overview on respective research approaches, we discuss their individual requirements, potential and restrictions. <s> BIB002 </s> Cyber-Physical Systems Security—A Survey <s> 4) Smart Cars Attacks: <s> Nowadays, auto insurance companies set personalized insurance rate based on data gathered directly from their customers' cars. In this paper, we show such a personalized insurance mechanism -- wildly adopted by many auto insurance companies -- is vulnerable to exploit. In particular, we demonstrate that an adversary can leverage off-the-shelf hardware to manipulate the data to the device that collects drivers' habits for insurance rate customization and obtain a fraudulent insurance discount. In response to this type of attack, we also propose a defense mechanism that escalates the protection for insurers' data collection. The main idea of this mechanism is to augment the insurer's data collection device with the ability to gather unforgeable data acquired from the physical world, and then leverage these data to identify manipulated data points. Our defense mechanism leveraged a statistical model built on unmanipulated data and is robust to manipulation methods that are not foreseen previously. We have implemented this defense mechanism as a proof-of-concept prototype and tested its effectiveness in the real world. Our evaluation shows that our defense mechanism exhibits a false positive rate of 0.032 and a false negative rate of 0.013. <s> BIB003
We reviewed about two dozen papers that discuss the security of smart cars. Most of the work done is abstract, theoretical, or simulation-based. Only a few present results of actual experiments on real cars BIB001 , BIB002 ,  ,  ,  . For a successful attack on a car, an attacker needs to gain access to the internal network physically, through the OBD-II port, media player, or USB ports, or wirelessly, through the Bluetooth or cellular interfaces. Once an attacker gets into the car's internal network, a plethora of attacks opportunities are open. a) DoS: DoS attacks can take on different forms whose impacts vary in safety-criticality. Koscher et al.  disabled CAN communication from and to the body control module (BCM) which resulted in a sudden drop from 40 to 0 mph on the speedometer. In addition, this attack also resulted in freezing the whole instrument panel cluster (IPC). For example, if the speedometer was at 60 mph before the attack, and the driver increased the speed, there will be no change in the speedometer and the driver might reach a dangerous speed level. b) False data injection: An example of this attack is displaying a false speed on the speedometer. An attacker would first intercept the actual speed update packet sent by the BCM, and then transmit a modified packet that had the false speed  . In addition, an attacker can forge the real status of the airbag system to appear healthy, even if the airbag had malfunctioned or was removed BIB002 . Another type FDI is shown in BIB003 where the authors showed how a passenger can manipulate the data collected by insurance dongles used for rate customization in a way that would resulted in an undeserved lower insurance price. c) Privacy invasion: Checkoway et al. BIB001 were able to exploit the cellular interface in the TCU and eavesdrop on in-car conversations. In addition, a report published by Ed Markey, a U.S. senator, reveals that car manufacturers store a large amount of private information such as driving history and cars' performance  .