Application Inference using ML based Side Channel Analysis

EasyChair Preprint 882

Abstract

Electromagnetic emissions(EM) have shown to reveal information about program running on device or as a defense mechanism to identify malicious code. It is shown to compromise security of many computing devices but only recently researchers have started exploring the interactions of DVFS and security. DVFS is integral part of modern system on chips to improve energy efficiency and battery lif.e The use of DVFS has been demonstrated as a countermeasure to power side channel attack on encryption engines. The use of fast DVFS enabled by on-chip regulator and adaptive clocking has been shown to deter extraction of encryption key in hardware accelerators. Similarly, authors have shown that by performing unconstrained overclocking/under-volting, faults could be injected during encryption to recover the secret key. We experimentally demonstrate (on a Snapdragon 820 development board) DVFS as a source of information leakage in software and utilized supervised machine learning (ML) based classification models to exploit the relationship between time-varying EM-emissions and DVFS states with applications characteristics to identify applications running on processor. Altogether, we are profiling legitimate applications so as to protect against untrusted application that can infer activities on a device through eavesdropping software events by hiding in the background

Keyphrases: Application Inference, DVFS, EM emissions, Snapdragon, SoC, Spectral features, machine learning, side-channel attacks

@booklet{EasyChair:882,
  author    = {Nikhil Chawla and Arvind Singh and Monodeep Kar and Nael Mizanur Rahman and Saibal Mukhopadhyay},
  title     = {Application Inference using ML based Side Channel Analysis},
  howpublished = {EasyChair Preprint 882},
  year      = {EasyChair, 2019}}