Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

James W. Dean, PhD


SCA attacks aim to recover some sort of secret information, often in the form of a cipher key, from a target device. Some of these attacks focus on either power-based leakage, or EM-based leakage. Neural networks have recently gained in popularity as tools in SCA attacks. Near-field EM probes with high-spatial resolution enable attackers to isolate physical locations above a processor. This enables attackers to exploit the spatial dependencies of algorithms running on said processor. These spatial dependencies result in different physical locations above a chip emanating different signal strengths. The strengths of different locations can be mapped using the performance of a neural network trained to detect secret information on near-field leakage data. Our contribution uses this mapping to identify ideal near-field leakage collection locations from which to conduct an attack. This paper demonstrates the effectiveness of this technique in reducing the time needed to conduct a successful EM SCA attack.

AFIT Designator



A 12-month embargo was observed.

Approved for public release. Case number on file.