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Detection of Potentially Compromised Computer Nodes and Clusters Connected on a Smart Grid, Using Power Consumption
Mohammed Almshari1, Georgios Tsaramirsis1, Adil Omar Khadidos1
1Department of IT, Faculty of Computing and IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|September 10, 2020
Summary
This study demonstrates that computer application activity and even malware infections can be identified by analyzing power consumption data. This method offers a privacy-preserving way for administrators to monitor computer lab usage and security.
Area of Science:
- Computer Science
- Electrical Engineering
- Cybersecurity
Background:
- Monitoring computer applications without user privacy violations is challenging, especially without direct access.
- Smart grids and Internet of Things (IoT) devices offer power consumption data for analysis.
Purpose of the Study:
- To determine if power consumption data can identify running applications and detect anomalies like malware.
- To establish a correlation between electric power usage and software activity.
Main Methods:
- Collected 48-hour real power consumption data from two PCs at a 1-second interval.
- Infected one PC with a virus to compare pre- and post-infection power data.
- Analyzed data using descriptive statistics, F-Test, ANOVA, and ARIMA.
Main Results:
- Successfully detected application types based on power consumption profiles.
- Identified the presence of a virus by analyzing power consumption changes.
- Differentiated between machine usage and idle states.
Conclusions:
- Power consumption analysis is a viable, privacy-preserving method for application identification and security monitoring.
- This technique can serve as an effective management tool for system administrators.
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