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Detecting “Harvest Now, Decrypt Later” Attacks Using AI/ML Models

Çağlar Arlı      -    24 Views

Detecting “Harvest Now, Decrypt Later” Attacks Using AI/ML Models

I’m researching strategies to detect the “Harvest Now, Decrypt Later” attack, also known as “store now, decrypt later” or “retrospective decryption.”

This surveillance approach involves acquiring and storing currently unreadable encrypted data, anticipating future breakthroughs in decryption technology that would render it readable. The hypothetical date for such decryption advancements is often referred to as Y2Q (a nod to Y2K).

Specifically, I’m interested in exploring AI and machine learning models that can identify signs of this attack.

Are there any existing models or techniques that can help detect instances of data harvesting with the intention of future decryption?