In particular, it aims to find software vulnerabilities using fuzzing, symbolic execution, and abstract interpretation techniques, in order to prevent unauthorised access to the network by shielding the network from malicious attacks and thus protecting the data flowing through the network. This PhD research is concerned with identifying software vulnerabilities, which compromise not only network security but also information security in IoT devices. Thus, the reliability of the embedded (distributed) software in IoT is a key issue in the system development. As Internet of Things (IoT) is now present in all technology sections, allowing different embedded systems to connect and exchange data, the chances of security breach have expanded to a large extent. Such systems are used in a wide range of applications such as airbag control systems, mobile phones, and high-end television sets. Our reliance on the correct functioning of embedded systems is growing rapidly. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information. Applications for this project are welcome from suitably qualified candidates worldwide. This research project has funding attached. Directly Funded Project (Students Worldwide).Hybrid Fuzzing Concurrent Software using Model Checking and Machine Learning.Verifying Cyber-attacks in CUDA Deep Neural Networks for Self-Driving Cars.Combining Concolic Testing with Machine Learning to Find Software Vulnerabilities in the Internet of Things.Automatic Detection and Repair of Software Vulnerabilities in Unmanned Aerial Vehicles.Automated Repair of Deep Neural Networks.Using Program Synthesis for Program Repair in IoT Security.Verification Based Model Extraction Attack and Defence for Deep Neural Networks.Exploiting Software Vulnerabilities at Large Scale.Designing Safe & Explainable Neural Models in NLP.Application Level Verification of Solidity Smart Contracts.
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