Quantum-enhanced anomaly detection for cybersecurity logs (BETH Dataset) - BC-976
Project type: ResearchDesired discipline(s): Engineering - computer / electrical, Engineering, Computer science, Mathematical Sciences
Company: AbaQus - Bloombase
Project Length: 4 to 6 months
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Vancouver, BC, Canada
No. of positions: 1
Desired education level: Master'sPhDPostdoctoral fellow
Open to applicants registered at an institution outside of Canada: No
About the company:
AbaQus Computing is a Vancouver-based startup developing quantum-enhanced optimization and machine learning tools for financial services and cybersecurity applications. We specialize in formulating real-world problems as Quadratic Unconstrained Binary Optimization (QUBO) and Constrained Quadratic Models (CQM) that can be solved on hybrid quantum platforms such as D-Wave. Our work has included feature selection for financial machine learning, portfolio optimization, and anomaly detection in security logs. We are currently collaborating with industry partners, including Bloombase, to explore post-quantum secure anomaly detection solutions for cyber defense. AbaQus focuses on bridging the gap between academic research and practical deployments, building systems that are both scientifically novel and directly relevant to industry needs.
Describe the project.:
We are developing a quantum-enhanced anomaly detection framework for cybersecurity log data (BETH dataset). The project involves designing QUBO and CQM formulations for anomaly detection, testing on hybrid quantum solvers (D-Wave), and benchmarking against classical ML baselines. Results will inform next-generation security systems with post-quantum resilience.
Required expertise/skills:
We are seeking a Master’s or PhD student with expertise in one or more of the following areas:
• Cybersecurity and anomaly detection methods (e.g., log analysis, intrusion detection)
• Applied machine learning and data mining (preferably time-series or graph-based ML)
• Quantum computing or optimization (QUBO, CQM, D-Wave, or related frameworks)
• Strong programming skills in Python, with experience using libraries such as scikit-learn, pandas, NumPy, and familiarity with D-Wave Ocean SDK considered an asset.
