It is an inescapable reality that information is now a significant presence for any business or organization. As a result, ensuring security is critical, and security models based on real-world data have become increasingly relevant. Due to the significant growth in cyber-attacks, artificial intelligence and machine learning-based approaches have become critical components in identifying security risks. Security-related benchmarks are critical for providing the optimal security applications and achieving an adequate level of security.
The Cyber Science Lab (CSL) at the University of Guelph has developed several datasets that are currently publicly available:
- OS X Malware Dataset
- Internet of Things Malware Dataset
- Advanced Persistent Threat (APT) Malware Dataset
- Cryptocurrency Malware Dataset