Provable Virus Detection: Using the Uncertainty Principle to Protect Against Malware
Vassilis Zikas
Abstract:
Protecting software from malware injection is the holy grail of modern computer security. Despite intensive efforts by the scientific and engineering community, the number of successful attacks continues to increase.
We have a breakthrough novel approach to provably detect malware. Our key idea is to use the very insertion of the malware itself to allow for the systems to detect it. In our opinion this is close in spirit to the famous Heisenberg Uncertainty Principle. The attackers, no matter how clever, no matter when or how they insert their malware, change the state of the system they are attacking and we show how this can always be detected. This fundamental idea is a game changer. Our approach does not rely on heuristics; instead, our scheme enjoys the unique property that it is proved secure in a formal and precise mathematical sense and with minimal and realistic CPU modification achieves strong provable security guarantees. Thus, we anticipate that our system will revolutionize both practice and theory of modern software protection.
This is joint work with Richard Lipton and Rafail Ostrovsky.