You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Low-level Anomaly Detection in Embedded Systems Using Machine Learning

Fulltext:


Authors:

Carlo Vitucci, Daniel Sundmark, Marcus Jägemar, Thomas Nolte

Research group:

Complex Real-Time Embedded Systems

Publication Type:

Conference/Workshop Paper

Venue:

11th International Conference on Computer Technology Application

Abstract

Let us consider an embedded system as a specific combination of hardware and software that is capable of consistently providing a certain service. Depending on the boundary conditions of the system, such as the working environment and the number of users served, we can say that the statistical distribution of resource usage is a characterization of the embedded system itself and its footprint. The consequence of this distinguishable footprint for embedded systems is that it becomes possible to use the statistical deviation of the resource usage distribution to identify anomalies. In this paper, we will analyze which Performance Metric Unit counters (e.g., CPU usage, memory usage) and resource profiles (e.g., system logs, performance metrics) are most characteristic for detecting a low-level anomaly: an alteration of the firmware working cycle or the propagation of a hardware error in the system. We will do this by using baseband products for radio access networks. We will demonstrate that using a machine learning model makes it possible to distinguish both the firmware cycle alteration and the hardware error reporting with an accuracy of more than 99% on unseen and new dataset.

Bibtex

@inproceedings{Vitucci7215,
author = {Carlo Vitucci and Daniel Sundmark and Marcus J{\"a}gemar and Thomas Nolte},
title = {Low-level Anomaly Detection in Embedded Systems Using Machine Learning},
editor = {IEEE},
month = {May},
year = {2025},
booktitle = {11th International Conference on Computer Technology Application},
url = {http://www.es.mdu.se/publications/7215-}
}