Cryptography is an important tool for safeguarding our data from attackers. The security of several modern cryptosystems relies on unproven properties of an algebraic structure called the class group of an algebraic number field. In the absence of proofs, tabulating class groups in order to generate numerical evidence of these unproven properties remains the best […]

This project aims to develop an innovative and novel cybersecurity solution tailored for Canadian small and medium-sized enterprises (SMEs), focusing on smart risk assessment and automated network segmentation. Many SMEs lack full visibility into their IT infrastructure, leaving them vulnerable to cyber threats. This project will study the challenges and needs of SMEs across different […]

In light of the recent concerning growth of cyberattacks in pace, scale, and sophistication empowered by contemporary Artificial Intelligence (AI) techniques with multiple complex hacking and penetration tools, Small and Medium Businesses (SMBs) suffer continuous and serious technical, financial, and societal hits as severe damages of these attacks. This surge in cyberattacks boosted by AI […]

The surge in data driven by economic expansion has increased the demand for fast, reliable internet. However, existing technologies struggle to keep up, causing video buffering, game lag, and slow file transfers. At the same time, rising cyber threats make data security a top concern, with high-profile breaches compromising millions of users and causing financial […]

The proposed project aims to develop a Quantum Convolutional Neural Network (QCNN)-based approach to detect vulnerabilities in smart contracts, which are critical components of blockchain technology. By leveraging quantum machine learning techniques, the project seeks to enhance the accuracy and efficiency of identifying security threats in smart contracts, such as reentrancy attacks and integer overflows. […]

As cyber threats grow more sophisticated, attackers increasingly exploit scheduled tasks to maintain persistence and evade detection. Traditional security measures struggle to distinguish between legitimate and malicious task executions, especially when attackers modify execution parameters. Additionally, identifying and attributing threats to known adversaries remains a complex and resource-intensive process, relying heavily on human analysts and […]

This project aims to enhance cybersecurity threat detection in response to attacks against Generative AI systems, by identifying novel characteristics in submissions to large language models (LLMs), and using these novel characteristics to generate innovative heuristics to scan for prominent threats to vulnerable GenAI system deployments. The challenge is twofold; first, augmenting existing extraction techniques […]

A quantum proof of knowledge (QPoK) is a protocol in quantum computing that allows one party (the prover) to convince another party (the verifier) that they possess certain knowledge without revealing the knowledge itself. Our research proposal aims to advance the science of QPoKs by developing both theoretical foundations and practical applications. We will explore […]

The main goal of this research endeavor is the development of a hybrid cloud infrastructure that incorporates quantum computing techniques into its design in order to increase the security, scalability, and level of usability of existing cloud computing systems. This specifically means integrating the more sophisticated aspects of quantum computing, such as quantum cryptography and […]

Quantum memories are key components in the development of a quantum network. Among their many uses, these memories will facilitate the synchronization of quantum signals at repeater nodes, which are a crucial technology for extending the long-distance reach of quantum communications. Quantum memories facilitate the transfer of quantum information from electromagnetic signals into matter-based quantum […]

This project aims to research and develop on building a real-time deepfake audio detection system that is capable of distinguishing between authentic and spoofed audio voices with the help of Quantum Machine Learning (QML). The primary goal is to identify the limitations of existing classical ML techniques and explore how QML can improve the different […]

We will investigate a decision procedure for the monitorability of timed-properties of software controlled systems. A property defines a behaviour that a system is expected to exhibit, or in some cases, to not exhibit. In Runtime Verification, a property is monitorable if it is possible to construct a monitor that can detect the system’s conformance […]