Using guided learning and reinforcement training techniques for deep analysis of reverse engineered malware to enable similarity analysis and prediction on next malware evolution focused on the adversary tactics modeled for defense actions - is the goal of Deep Learning Malware
- Once the malware is released, other adversaries repurpose and the threat evolves.
- Pattern matching is static, source code for malware is rare requiring reverse engineering (RE).
- Advances in RE tools and machine learning have enabled better analysis – including all realms of malware potential.
- Deep Learning Malware behaviors lead to better/implementable remediations.
- Enables sharing across energy domains, IT/OT, crowd sourcing and faster understanding of malware targeting energy to develop better indicators and courses of action
- Characterized and Eliminate Malware
- Structured Threat for Visual, Sharable, Actionable, and Implementable (IT/OT)
- All Possible Paths/Constraints
- Deep Learning Malware uses recent ML concepts to characterize harder to change, malware behavior in implementable indicators and courses of action