Available at: https://digitalcommons.calpoly.edu/theses/3353
Date of Award
6-2026
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Stephen Beard
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
Smartphones are frequently connected to external, untrusted charging hardware, creating opportunities for side-channel attacks that do not require malware or direct access to device data. Charger Surfing, a recently proposed charging-line power analysis side-channel attack, reported high accuracy in inferring touchscreen input from voltage measurements collected from a smartphone’s charging cable; however, the reproducibility and robustness of these results under different conditions remain unclear. This thesis presents an independent replication and evaluation of Charger Surfing, including the development of an end-to-end data collection pipeline consisting of a modified charging cable, oscilloscope-based recordings, custom Android app, automated trace processing, and convolutional neural network classification framework. Experiments were conducted across multiple users, devices, collection sessions, and interaction styles to evaluate signal observability and model generalization. Results show that charging-line measurements can contain information associated with touchscreen input, achieving up to 95.24% same-day test accuracy and 96.20% second-day accuracy on a Google Pixel 4a; however, performance often degraded significantly across collection sessions and devices, with accuracy approaching chance levels in some settings, suggesting that Charger Surfing is highly dependent on collection conditions and that robustness remains a major challenge.
Included in
Computational Engineering Commons, Computer and Systems Architecture Commons, Digital Circuits Commons, Hardware Systems Commons, Other Computer Engineering Commons