Discovery Early Career Researcher Award - Grant ID: DE170101081
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis t ....Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis to efficiently and precisely analyse large-scale programs according to clients’ needs, thereby allowing compilers to generate safe, reliable and secure code. This project is expected to advance value-flow analysis for industrial-sized software, improve software reliability and security, and benefit Australian software systems and industries.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190100046
Funder
Australian Research Council
Funding Amount
$387,000.00
Summary
Fortifying our digital economy: advanced automated vulnerability discovery. This project aims to enable security researchers to detect critical vulnerabilities in large software systems with maximal efficiency, cost-effectively, and with known statistical accuracy. The aim is to develop advanced high-performance fuzzers that effectively thwart malware attacks, ransomware epidemics, and cyber terrorism by exposing security flaws before they can commence. The project will employ a well-established ....Fortifying our digital economy: advanced automated vulnerability discovery. This project aims to enable security researchers to detect critical vulnerabilities in large software systems with maximal efficiency, cost-effectively, and with known statistical accuracy. The aim is to develop advanced high-performance fuzzers that effectively thwart malware attacks, ransomware epidemics, and cyber terrorism by exposing security flaws before they can commence. The project will employ a well-established statistical framework utilised in ecology research to provide fundamental insights to boosting the efficiency of software vulnerability discovery, and on the trade-off between investing more resources and gaining better cyber security guarantees. As our reliance on new technologies is ever growing, this project equips Australia to curb cyber crime cost-effectively.Read moreRead less
Secure user authentication with continuous adaptive risk evaluation. Users typically authenticate to any given system only once - when they first access it (for example, through providing a password or fingerprint). The prevalence of single sign-on further allows this single authentication to be sufficient for access to multiple systems. Thus an adversary can obtain a large degree of access from stealing a single password, hijacking a user's session, or even simply borrowing their phone. This pr ....Secure user authentication with continuous adaptive risk evaluation. Users typically authenticate to any given system only once - when they first access it (for example, through providing a password or fingerprint). The prevalence of single sign-on further allows this single authentication to be sufficient for access to multiple systems. Thus an adversary can obtain a large degree of access from stealing a single password, hijacking a user's session, or even simply borrowing their phone. This project aims to develop a continuous authentication approach based on user behaviour - typical interactions plus biometrics (for example, keystroke dynamics) - combined with a risk adaptive assessment of the resources being accessed, resulting in re-authentication requests in the event of a suspected compromise.Read moreRead less