Malicious attacks exhibit behavioral patterns that deviate from standard system or network activity, making them identifiable when properly analyzed. Although traditional signature-based methods can detect many well-known threats, adversaries continuously evolve their tactics, techniques, and procedures, developing novel, often stealthy methods that bypass conventional defenses and move laterally without detection.
Since its establishment in 2022, the BCCC has been committed to uncovering the foundational causes of cyberattacks and producing actionable insights that strengthen future detection and prevention. Our technical approach leverages machine learning, artificial intelligence, big data analytics, and behavioral modeling to capture deviations from normal operational patterns under the Universal Behavior Profiling (UBP) framework. This enables us to characterize both benign and malicious behaviors with high precision.
In parallel, we advance the field of secure and trustworthy AI, ensuring that AI-powered detection systems remain robust against adversarial manipulation, data poisoning, evasion attacks, and other emerging threats. Our research aims to build AI models that are not only powerful for security tasks but are themselves secure, interpretable, and resilient.
What makes us unique:
- Universal Behavior Profiling (UBP): Propose, design, develop, and release fundamental anomaly detection, characterization, and identification models using Behavior Profiling (BP)
- Cybersecurity Data Analyzers: Propose, design, and develop cybersecurity data analyzers (Open-source projects in GitHub)
- Cybersecurity Datasets: Design, create, and release Cybersecurity Datasets
- Cybersecurity Awareness and Training Series:
- UCS Books
- UCS Articles
- UCS Workshops
- UCS Blogs
- UCS Cartoon Awards
November 17, 2025
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November 10, 2025
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Acknowledgement
We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).