Automated security analysis of hardware systems

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This research project proposes to investigate and create machine learning (ML)-backed tools to perform automatedsecurity analysis of embedded and IoT devices, from both a hardware and software perspective. The main challengesinvolved with analysing embedded systems stem from the diversity and tight coupling of hardware and software involved.Another very real security threat is caused by the supply chain involved in producing these IoT devices. The componentsthat make up these systems are made by a wide range of companies, all with different security standards andguarantees. ML systems have a similar problem due to models being based on a myriad of different software packagesfrom different authors. This outsourcing poses a security risk. An automated security analysis system forhardware/software can alleviate this problem by making it easier for manufacturers to test their products, and by allowingcompanies using these suppliers to test individual components up to a necessary security standard. We propose to usehierarchical reinforcement learning architecture to tackle the problem of vulnerability discovery from 'both ends'. Namely,the bottom-level hierarchy would be responsible for discovery and exploit of low-level vulnerabilities and bugs (such asbuffer-overflow, or use-after-free susceptible chunks of code), while the top-level hierarchy will attempt to link thesetogether to create a more complex exploit.