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Implementation and Evaluation of an Assisting Fuzzer Harness Generation Tool for AUTOSAR Code
(2024)
The digitalization in vehicles tends to add more connectivity such as over-the-air (OTA) updates. To achieve this digitization, each ECU (Electronic Control Unit) becomes smarter and needs to support more and more different externally available protocols such as TLS, which increases the attack surface for attackers. To ensure the security of a vehicle, fuzzing has proven to be an effective method to discover memory-related security vulnerabilities. Fuzzing the software run- ning on a ECU is not an easy task and requires a harness written by a human. The author needs a deep understanding of the specific service and protocol, which is time consuming. To reduce the time needed by a harness author, this thesis aims to develop FuzzAUTO, the first assistant harness generation tool targeting the AUTOSAR (AUTomotive Open System ARchitecture) BSW (Basic Software) to support manual harness generation.
The aim of this study was to develop a biomechanically validated finite element model to predict the biomechanical behaviour of the human lumbar spine in compression.
For validation of the finite element model, an in vitro study was performed: Twelve human lumbar cadaveric spinal segments (six segments L2/3 and six segments L4/5) were loaded in axial compression using 600 N in the intact state and following surgical treatment using two different internal stabilisation devices. Range of motion was measured and used to calculate stiffness.
A finite element model of a human spinal segment L3/4 was loaded with the same force in intact and surgically altered state, corresponding to the situation of biomechanical in vitro study.
The results of the cadaver biomechanical and finite element analysis were compared. As they were close together, the finite element model was used to predict: (1) load-sharing within human lumbar spine in compression, (2) load-sharing within osteoporotic human lumbar spine in compression and (3) the stabilising potential of the different spinal implants with respect to bone mineral density.
A finite element model as described here may be used to predict the biomechanical behaviour of the spine. Moreover, the influence of different spinal stabilisation systems may be predicted.