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Found 4 matching student topics

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Fine-grained software vulnerability detection using deep learning techniques

Software vulnerability is a major threat to the security of software systems. Thus, the successful prediction of security vulnerability is one of the most effective attack mitigation solutions. Existing approaches for software vulnerability detection (SVD) can be classified into static and dynamic methods. Powered by AI capabilities, especially with the advancement of machine learning techniques, current software has been produced with more sophisticated methodologies and components. This has made the automatic vulnerability proneness prediction even more challenging. Recent research efforts …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Science
School
School of Computer Science

Security analysis of open-source software

Several open-source projects drive modern-day IT applications. However, some open-source projects get compromised by malicious attackers, who include malware to the code to compromise the security of the application users.This project will investigate approaches for securing the open-source software.

Study level
Honours
Faculty
Faculty of Science
School
School of Computer Science

Unified def-site and use-site security policies for component-based software systems

Securing the information manipulated by computer systems, such as privacy and integrity in social software, is a challenge. Traditional methods to impose limits on the information disclosure, such as access control lists, firewalls, and cryptography, provide no guarantees about information propagation. For instance, cryptography provides no guarantees about the confidentiality of the data are given once it is decrypted.Information flow control (IFC) is the problem of ensuring secure information flow according to specified policies within computer systems. Modern applications are …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Science
School
School of Computer Science

Cybersecurity for open-source software using machine learning and AI

People are increasingly using open-source software in businesses and industries. These software programs are made by a community of developers and are managed by platforms like PyPI and npm. However, there is a worry about the safety of these programs because hackers add harmful code to compromise security and steal important data. This project explores approaches to detect harmful open-source projects using machine learning and AI.

Study level
Honours
Faculty
Faculty of Science
School
School of Computer Science

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