Reproduce “ATHAFI: Agile Threat Hunting And Forensic Investigation” on OpenResearch

About this paper

Rami Puzis, Polina Zilberman, Yuval Elovici

The ATHAFI framework proposes a semi-automated system for agile threat hunting and forensic investigation, dynamically generating investigative workflows and attack hypotheses by integrating real-time cyber threat intelligence (CTI) with organizational forensic data. It demonstrates a method for unifying CTI analysis, intelligent hypothesis generation, and targeted data collection into a coherent process, showcased through the integration of commercial off-the-shelf security products.

Key insights

• The diversity of environments and objects in training data is more critical than the sheer number of demonstrations for achieving zero-shot generalization in robotic manipulation policies.
• Effective data collection strategies, focusing on diversity, can achieve high success rates (around 90%) on new tasks with moderate data volumes in imitation learning.
• Empirically investigating data scaling laws provides actionable insights for optimizing data acquisition in real-world robotic applications, addressing the trade-offs between data quantity and quality.

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