External Research Grants

Compilers are a key technology of software development. They are relevant for not only general purpose programming languages (like C/Java) but also many domain specific languages. Compilers are error-prone, especially concerning less-used language features. Existing compiler testing techniques often rely on weak test oracles which prevents them from finding deep semantic errors. The project aims to develop a novel specification-based fuzzing method named SpecTest for compilers. SpecTest has three components: an executable specification of the language, a fuzzing engine which generates test cases for programs in the language, and a code mutator which generates new programs for testing the compiler. SpecTest identifies compiler bugs by comparing the abstract execution of the specification and concrete execution of compiled program. Furthermore, with the mutator, SpecTest can systematically test those less-used language features.

Today’s malware analysis tools, especially those on kernel attacks, face the barrier of insufficient code path coverage to fully expose malicious behaviours, as that requires systematic exploration of kernel states. Although symbolic execution is the well-established solution for benign programs’ code coverage, it does not overcome that barrier because of its susceptibility to attacks from the running target under analysis and incapability of managing complex kernel execution. This project aims to innovate cutting-edge techniques to automatically and systematically generate code paths for maliciously-influenced kernel behaviours.

Control-Flow Integrity (CFI) enforcement is a promising technique in producing trustworthy software. This project focuses on function signature recovery, which is a critical step in CFI enforcement when source code is not available. Current approaches rely on the assumption of matching function signatures at caller and callee sites in an executable; however, various compiler optimisations violate well-known calling conventions and result in unmatched function signatures recovered. The project aims to design and implement an automatic system to produce CFI-enforced program executables.

Artificial Intelligence (AI) technologies have been under rapid development thanks to machine learning based on deep neural networks and their applications. Despite the exceptional performance of deep neural networks, these complex models are often beyond human understanding and thus work in a black-box manner. The research aims to address the problem of explaining AI for AI system designers and expert AI system users who are required to know how AI makes decisions.

This project aims to provide secure remote access control over identity information of Internet-of-Things (IoT) devices to prevent sensitive information from being stolen.

Software development today relies on Application Programming Interfaces (APIs), and identifying suitable APIs to use can directly influence the success or failure of a software development project. While a large number of third-party APIs are available on the internet, selecting suitable APIs for a project can be challenging. This research proposes a big-data, deep-learning, and exploratory-search approach for API recommendation called DeepSense to improve software developers’ productivity, and the success of this project will benefit the software engineering and artificial intelligence research community, software developers, and institutions developing IT solutions.

This project aims to optimise response of fire engines and ambulances to medical and fire incidents in a prioritised manner.

Awarded under the Microsoft Research Asia Collaborative Research Program 2019, this project aims to extend existing video analytics solutions to be able to process videos of people entering a building and automatically classify them into different categories, in real-time and with as minimal prior knowledge of the people as possible.

Project Title: National Satellite of Excellence in Mobile Systems Security and Cloud Security Supported by NRF, the National Satellite of Excellence in Mobile Systems Security & Cloud Security aims to develop a technology pipeline that would address the mobile system security and mobile cloud security needs for real-time monitoring/decision systems used in critical smart nation applications. It will focus on research in the following core competencies:

• Privacy-preserving access and search of encrypted data
• Privacy-preserving computation over encrypted data
• Applications of privacy-preserving technologies in in-home elderly monitoring systems

This project will pioneer new capabilities in real-time, ultra-low power, pervasive sensing (e.g., tracking a user’s pointing gestures with cm-level accuracy), by building technologies that enable a collection of resource-constrained wearable and cheap IoT devices to collaboratively execute complex machine intelligence tasks. The research will advance Singapore’s capabilities in areas such as smart manufacturing and smart cities.