Detection of injection attacks on in-vehicle network using data analytics

We investigate the possibility of detection of injection attacks using data analytics techniques in this thesis. The automotive industry is innovating the modern vehicles towards connectivity by interfacing them with various external entities. These entities are exposing the automobile to cyber attacks instead of ensuring its safety. Therefore it is important to consider the security aspect while developing these interfaces. Firstly, we try understand the automobile network architecture and the possible security threats associated with it. Next, we examine the various possible cyber-attacks on automobiles described in the literature. We experiment and analyze the attack scenarios by performing injection attacks on a vehicle. We collect the data during the injection attacks and apply multiple data analysis techniques. These techniques build a model based on data during normal operation. The observations from the data collected during injection attacks is fit into these techniques. The data points that do not fit the model are termed as attack points. Finally we examine and analyze the results and their accuracy in detecting injection attacks

Bellagio Memorandum on Motor Vehicle Policy

Presents a consensus document on preferred government policies for shaping the future of motor vehicle technology worldwide. Details 43 key principles for policymakers looking to speed the transition to clean vehicles

On the Performance of Detecting Injection of Fabricated Messages into the CAN Bus

There have been several public demonstrations of attacks on connected vehicles showing the ability of an attacker to take control of a targeted vehicle by injecting messages into their CAN bus. In this paper, using injected speed reading and RPM reading messages in in-motion vehicle, we examine the ability of the Pearson correlation and the unsupervised learning methods k-means clustering and HMM to differentiate \u27no-attack\u27 and \u27under-attack\u27 states of the given vehicle. We found that the Pearson correlation distinguishes the two states, the k-means clustering method has an acceptable accuracy but high false positive rate and HMM detects attacks with acceptable detection rate but has a high false positive in detecting attacks from speed readings when there is no attack. The accuracy of these unsupervised learning methods are comparable to the ones of the supervised learning methods used by CAN bus IDS suppliers. In addition, the paper shows that studying CAN anomaly detection techniques using off-vehicle test facilities may not properly evaluate the performance of the detection techniques. The results suggest using other features besides the data content of the CAN messages and integrate knowledge about how the ECU collaborate in building effective techniques for the detection of injection of fabricated message attacks

An Extended Survey on Vehicle Security

The advanced electronic units with wireless capabilities inside modern vehicles have, enhanced the driving experience, but also introduced a myriad of security problems due to the inherent limitations of the internal communication protocol. In the last two decades, a number of security threats have been identified and accordingly, security measures have been proposed. In this paper, we provide a comprehensive review of security threats and countermeasures for the ubiquitous CAN bus communication protocol. Our review of the existing literature leads us to a observation of an overlooked simple, cost-effective, and incrementally deployable solution. Essentially, a reverse firewall, referred to in this paper as an icewall, can be an effective defense against a major class of packet-injection attacks and many denial of service attacks. We cover the fundamentals of the icewall in this paper. Further, by introducing the notion of human-in-the-loop, we discuss the subtle implications to its security when a human driver is accounted for

Defending Vehicles Against Cyberthreats: Challenges and a Detection-Based Solution

The lack of concern with security when vehicular network protocols were designed some thirty years ago is about to take its toll as vehicles become more connected and smart. Today as demands for more functionality and connectivity on vehicles continue to grow, a plethora of Electronic Control Units (ECUs) that are able to communicate to external networks are added to the automobile networks. The proliferation of ECU and the increasing autonomy level give drivers more control over their vehicles and make driving easier, but at the same time they expand the attack surface, bringing more vulnerabilities to vehicles that might be exploited by hackers. Possible outcomes of a compromised vehicle range from personal information theft to human life loss, raising the importance of automotive cybersecurity to a whole different level. Therefore, network safety has become a necessary and vital consideration of a vehicle. This project is two-fold: the first half will focus on the background of vehicle cybersecurity, characteristics of vehicular networks that could be leveraged during a hacking process, including ECU, Controller Area Network (CAN bus) and On-Board Diagnostics (OBD). It also discusses and evaluates previous hacking experiments conducted by researchers and their proposed countermeasures. The second half is an evaluation of approaches to design an Intrusion Detection System (IDS). The aim of this project is to find an effective and suitable solution todefend vehicles against various types of cyber threats

Vehicle Safe-Mode, Limp-Mode in the Service of Cyber Security

This paper describes a concept for vehicle safe-mode, that may help reduce the potential damage of an identified cyber-attack. Unlike other defense mechanisms, that try to block the attack or simply notify of its existence, our mechanism responds to the detected breach, by limiting the vehicle\u2019s functionality to relatively safe operations, and optionally activating additional security counter-measures. This is done by adopting the already existing mechanism of Limp-mode, that was originally designed to limit the potential damage of either a mechanical or an electrical malfunction and let the vehicle \u201climp back home\u201d in relative safety. We further introduce two modes of safe-modemoperation: In Transparent-mode, when a cyber-attack is detected the vehicle enters its pre-configured Limp-mode; In Extended-mode we suggest to use custom messages that offer additional flexibility to both the reaction and the recovery plans. While Extended-mode requires modifications to the participating ECUs, Transparent-mode may be applicable to existing vehicles since it does not require any changes in the vehicle\u2019s systems\u2014in other words, it may even be deployed as an external component connected through the OBD-II port. We suggest an architectural design for the given modes, and include guidelines for a safe-mode manager, its clients, possible reactions, and recovery plans. We note that our system can rely upon any deployed anomaly-detection system to identify the potential attack