Quantum-enhanced anomaly detection for cybersecurity logs (BETH Dataset) - BC-976
Genre de projet: RechercheDiscipline(s) souhaitée(s): Génie - informatique / électrique, Génie, Informatique, Sciences mathématiques
Entreprise: AbaQus - Bloombase
Durée du projet: 4 à 6 mois
Date souhaitée de début: Dès que possible
Langue exigée: Anglais
Emplacement(s): Vancouver, BC, Canada
Nombre de postes: 1
Niveau de scolarité désiré: MaîtriseDoctoratRecherche postdoctorale
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: No
Au sujet de l’entreprise:
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.
Veuillez décrire le projet.:
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.
Expertise ou compétences exigées:
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.
