Abstract
We propose an autoencoder based approach to anomaly detection in smart grid systems. Data collecting sensors within smart home systems are susceptible to many data corruption issues, such as malicious attacks or physical malfunctions. By applying machine learning to a smart home or grid, sensor anomalies can be detected automatically for secure data collection and sensor-based system functionality. In addition, we tested the effectiveness of this approach on real smart home sensor data collected for multiple years. An early detection of such data corruption issues is essential to the security and functionality of the various sensors and devices within a smart home.
Original language | English |
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Title of host publication | Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020 |
Pages | 67-70 |
Number of pages | 4 |
ISBN (Electronic) | 9780738142647 |
DOIs | |
State | Published - Dec 2020 |
Event | 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020 - Virtual, Chennai, India Duration: Dec 14 2020 → Dec 16 2020 |
Publication series
Name | Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020 |
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Conference
Conference | 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020 |
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Country/Territory | India |
City | Virtual, Chennai |
Period | 12/14/20 → 12/16/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Anomaly Detection
- Cybersecurity
- Machine Learning
- Sensors
- Smart Home
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Information Systems and Management
- Electrical and Electronic Engineering
- Safety, Risk, Reliability and Quality
- Control and Optimization