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Recent advances in automotive technologies have paved way to a new era of connectivity. Advanced Driver Assistance Systems are getting deployed in automobiles; many companies are developing driverless cars; connected cars are no more a work of mere research. [1] Vehicle manufacturers are developing ways to interface mobile devices with vehicles. However, all these advances in technology has introduced security risks. Unlike traditional computing systems, the security risk of an automobile can be fatal and can result in loss of lives [2]. The in-vehicle network of an automobile was originally designed to operate in a closed environment and hence network security was not considered during its design [3]. Several studies have already shown that an in-vehicle network can be easily compromised and an intruder can take full control of the vehicle. Researchers are working on various ways to solve this problem. Securing the in-vehicle communication by encrypting the messages is one such way.

Some of the recent studies reveal that it is possible to access the in-vehicle networks and inject malicious messages to alter the behavior of the vehicle. Researchers have shown that, it is possible to hack a car’s communication network and remotely take control of brake, steering, power window systems, etc. Hence, it becomes inevitable to implement schemes that detect anomalies and prevent attacks on Controller Area Network (CAN). Our work explores the complete anomaly detection process for CAN. We cover the techniques followed, available tools and challenges at every stage. Beginning with what makes CAN protocol vulnerable, we discuss case studies about attacks on CAN with major focus on Denial of Service (DoS) attack. We analyze the pattern of normal CAN messages obtained from real vehicle, along with patterns of simulated attack data using different methods/tools.

Automobiles are getting more and more sophisticated with increased demand for more comfort and safety by customers. Due to this, the automotive Electronic Control Units (ECU) and the software applications running on these ECUs have become more complex and computationally more intensive. This has resulted in Original Equipment Manufacturers (OEMs) migrating to multicore platforms. Optimal usage of multicore platform necessitates the design of new scheduling algorithms. In the past decade, different approaches to implement hard real time scheduling in automotive domain have been proposed for single core as well as multicore architectures. We explore different scheduling techniques proposed so far which are relevant to automotive domain and also, provide a taxonomy of these scheduling algorithms, which will help the automotive design engineer to make an informed decision.