How Machine Learning Is Reshaping Side-Channel Attacks
A survey of compromising-emanation attacks, AI-driven detection, and their strategic-intelligence implications
The claim
Machine learning put compromising emanations within reach of a phone. It is also the best defense against them.
For fifty years the unintended electromagnetic, acoustic, optical and power signals leaking from electronics were a niche concern, held in check by shielding, separation distances and the sheer difficulty of turning noise into intelligence. The survey argues that machine learning has dissolved that difficulty: the core problem in every side-channel attack is pattern recognition under noise, which is precisely what deep learning does best, and commodity software-defined radios and smartphones have collapsed the cost of collection to match.
“Machine learning is a dual-use force multiplier for compromising emanations.”
Read the first page. Full publication released upon request.