RaDaR: A Real-Word Dataset for AI powered Run-time Detection of Cyber-Attacks
Sareena Karapoola, Nikhilesh Singh, Chester Rebeiro, Veezhinathan Kamakoti · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Artificial Intelligence techniques on malware run-time behavior have emerged as a promising tool in the arms race against sophisticated and stealthy cyber-attacks. While data of malware run-time features are critical for research and benchmark comparisons, unfortunately, there is a dearth of real-world datasets due to multiple challenges to their collection. The evasive nature of malware, its dependence on connected real-world conditions to execute, and its potential repercussions pose significant challenges for executing malware in laboratory settings. Consequently, prior open datasets rely on isolated virtual sandboxes to run malware, resulting in data that is not representative of malware behavior in the wild.