Automotive firmware extraction and analysis techniques

An intricate network of embedded devices, called Electronic Control Units (ECUs), is responsible for the functionality of a modern vehicle. Every module processes a myriad of information and forwards it on to other nodes on the network, typically an automotive bus such as the Controller Area Network (CAN). Analysing embedded device software, and automotive in particular, brings many challenges. The analyst must, especially in the notoriously secretive automotive industry, first lift the ECU firmware from the hardware, which typically prevents unauthorised access. In this thesis, we address this problem in two ways: - We detail and bypass the access control mechanism used in diagnostic protocols in ECU firmware. Using existing diagnostic functionality, we present a generic technique to download code to RAM and execute it, without requiring physical access to the ECU. We propose a generic firmware readout framework on top of this, which only requires access to the CAN bus. - We analyse various embedded bootloaders and combine dynamic analysis with low-level hardware fault attacks, resulting in several fault-injection attacks which bypass on-chip readout protection. We then apply these firmware extraction techniques to acquire immobiliser firmware by two different manufacturers, from which we reverse engineer the DST80 cipher and present it in full detail here. Furthermore, we point out flaws in the key generation procedure, also recovered from the ECU firmware, leading to a full key recovery based on publicly readable transponder pages

Exploitation of Unintentional Information Leakage from Integrated Circuits

Unintentional electromagnetic emissions are used to recognize or verify the identity of a unique integrated circuit (IC) based on fabrication process-induced variations in a manner analogous to biometric human identification. The effectiveness of the technique is demonstrated through an extensive empirical study, with results presented indicating correct device identification success rates of greater than 99:5%, and average verification equal error rates (EERs) of less than 0:05% for 40 near-identical devices. The proposed approach is suitable for security applications involving commodity commercial ICs, with substantial cost and scalability advantages over existing approaches. A systematic leakage mapping methodology is also proposed to comprehensively assess the information leakage of arbitrary block cipher implementations, and to quantitatively bound an arbitrary implementation\u27s resistance to the general class of differential side channel analysis techniques. The framework is demonstrated using the well-known Hamming Weight and Hamming Distance leakage models, and approach\u27s effectiveness is demonstrated through the empirical assessment of two typical unprotected implementations of the Advanced Encryption Standard. The assessment results are empirically validated against correlation-based differential power and electromagnetic analysis attacks