Effective analytics for real-life time series anomaly detection

Anomaly detection methods address the need for automatic detection of unusual events with applications in cybersecurity. This project aims to address the efficacy of existing models when applied to real-life data. The goal is to generate new knowledge in the field of time series anomaly detection [1,2] through the invention of methods that effectively learn to generalise patterns of normal from real-life data. Expected outcomes include improved anomaly detection methods with reduced false positives, thereby reducing the costs associated with investigating false positives and minimising resource wastage. These improvements should provide significant benefits, such as reducing severe losses due to cybercrimes and enhancing national security for Australians.

[1] Darban, Z. Z., Webb, G. I., Pan, S., Aggarwal, C. C., & Salehi, M. (2025). CARLA: Self-supervised contrastive representation learning for time series anomaly detection. Pattern Recognition, 157, 110874.

[2] Zamanzadeh Darban, Z., Webb, G. I., Pan, S., Aggarwal, C., & Salehi, M. (2022). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys.