Malicious attacks display discernible behavior patterns that diverge from regular activity within a system or network, making them distinguishable. While companies can typically detect harmful behavior through signatures closely linked to specific types of well-known attacks, attackers continuously develop new techniques, methods, and procedures that enable them to penetrate vulnerable environments and move laterally without detection.
At BCCC, we strive to identify the underlying causes of such attacks and provide insights for future detection and prevention. Our technical approach involves analyzing deviations from normal daily activities using machine learning, artificial intelligence, big data, and analytics to profile both normal and abnormal behavior under Universal Behavior Profiling (UBP) in order to detect malicious behavior. Additionally, our awareness and training approach aims to produce a diverse collection of resources for researchers and readers of all backgrounds under the title Understanding Cybersecurity Series (UCS).
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
- Understanding Cybersecurity Series (UCS):
- UCS Books
- UCS Articles
- UCS Blogs
We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).