Open Access

Modern cars include more and more features that first emerged from the consumer electronics industry. Technologies like Bluetooth and Internet-connected services found their way into the vehicle industry. The secure implementation of these functions presents a great challenge for the manufacturers because products originating from the consumer industry can often not be easily transferred to the safety-sensitive traffic environment due to security concerns. However, common automotive interfaces like the diagnostics port are now also used to implement new services into the car. With dongles designed to read out certain vehicle data and transfer it to the Internet via the cellular network, the owner can access information about gas consumption or vehicle location through a mobile phone app, even when he is away from the car. This paper wants to emphasize new threats that appear due to the ongoing interconnection in modern cars by discussing the security of the diagnostics interface in combination with the use of an Internet-connected dongle. Potential attack vectors, as well as proof-of-concept exploits will be shown and the implications of security breaches on the safe state of the vehicle will be investigated.

The modular addition is a popular building block when designing lightweight ciphers. While algorithms mainly based on the addition can reach very high performance, masking their implementations results in a huge penalty. Since efficient protection against side-channel attacks is a requirement in lots of use cases, we focus on optimizing the Boolean masking of the modular addition. Contrary to recent related work, we target evolving a masked full adder instead of parts of a parallel prefix adder. We study how techniques typically found in neural network evolution and genetic algorithms can be adapted in order to help in evolving an efficiently masked adder. We customize a well-known neuroevolution algorithm, develop an optimized masked adder with our new approach and implement the ChaCha20 cipher on an ARM Cortex-M3 controller. We compare the performance of the protected neuroevolved implementation to solutions found by traditional search methods. Moreover, the leakage of our new solution is validated by a t-test conducted with a leakage simulator. We present under which circumstances our masked implementation outperforms related work and prove the feasibility of successfully using neuroevolution when searching for complex Boolean networks.

In this work, we present our benchmarking results for the ten finalist ciphers of the Lightweight Cryptography (LWC) project initiated by National Institute of Standards and Technology (NIST). We evaluate the speed and code size of various software implementations on five different platforms featuring four different architectures. Moreover, we benchmark the dynamic memory utilization of the remaining NIST LWC algorithms on one 32-bit ARM controller. We describe our test cases and methodology and provide some information regarding the design and properties of the finalists before showing and discussing our results. Altogether, we evaluated almost 300 implementations of the 3rd round candidates and pick the most appropriate and best (primary) implementation of each cipher for our comparisons. We include a variant of AES-GCM in our benchmarking in order to be able to compare the state-of-the-art to the novel LWC ciphers. Our research gives an overview over the performance of the latest software implementations of the NIST LWC finalists and shows under which circumstances which candidate is performing the best in our individual test cases. Additionally, we make all benchmarking results, the code for our test framework and every tested implementation available to the public to ensure a transparent testing process.

This paper introduces a custom framework for benchmarking software implementations from the National Institute of Standards and Technology (NIST) Lightweight Cryptography (LWC) project on embedded devices. We present the design and core functions of the framework and apply it to various NIST LWC authenticated encryption with associated data (AEAD) ciphers. Altogether, we evaluate the speed of 213 submitted algorithm vari-ants on four different microcontroller units (MCUs), including 32 bit ARM and 8 bit AVR architectures. To allow a more meaningful comparison, we also conduct code size tests on all four boards and RAM utilization tests on one test platform.

Diagnostic protocols in automotive systems can offer a huge attack surface with devastating impacts if vulnerabilities are present. This paper shows the application of active automata learning techniques for reverse engineering system state machines of automotive systems. The developed black-box testing strategy is based on diagnostic protocol communication. Through this approach, it is possible to automatically investigate a highly increased attack surface. Based on a new metric, introduced in this paper, we are able to rate the possible attack surface of an entire vehicle or a single Electronic Control Unit (ECU). A novel attack surface metric allows comparisons of different ECUs from different Original Equipment Manufacturers (OEMs), even between different diagnostic protocols. Additionally, we demonstrate the analysis capabilities of our graph-based model to evaluate an ECUs possible attack surface over a lifetime.