Anomaly Detection in Embedded Systems Using Power and Memory Side Channels

Jiho Park, Virinchi Roy Surabhi, Prashanth Krishnamurthy, Siddharth Garg, Ramesh Karri, Farshad Khorrami

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We propose multi-modal anomaly detection in embedded systems using time-correlated measurements of power consumption and memory accesses. Time series of power consumption of the processor and memory accesses between L2 cache and memory bus under known-good conditions are used to train one-class support vector machine (SVM) and isolation forest classifiers. These side channels have complementary anomaly detection capabilities. Experiments on a high-fidelity processor emulator show that the method accurately detects anomalies.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE European Test Symposium, ETS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728143125
StatePublished - May 2020
Event2020 IEEE European Test Symposium, ETS 2020 - Tallinn, Estonia
Duration: May 25 2020May 29 2020

Publication series

NameProceedings of the European Test Workshop
ISSN (Print)1530-1877
ISSN (Electronic)1558-1780


Conference2020 IEEE European Test Symposium, ETS 2020


  • Anomaly detection
  • cybersecurity
  • memory access
  • power consumption
  • support vector machine (SVM)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Software


Dive into the research topics of 'Anomaly Detection in Embedded Systems Using Power and Memory Side Channels'. Together they form a unique fingerprint.

Cite this