Device-Centric Firmware Malware Detection for Smart Inverters using Deep Transfer Learning
Conference
·
· 2022 IEEE Design Methodologies Conference (DMC)
- Texas A&M University-Kingsville, TX (United States); Texas A&M University-Kingsville
- Texas A&M University-Kingsville, TX (United States)
- Soongsil University, Seoul (South Korea)
- Minnesota State University, Mankato, MN (United States)
Since future power grids are inverter-dominant grids and inverters are getting smarter by incorporating remote access and seamless firmware update, it is anticipated that malware attackers will directly target smart inverters. However, malware threats targeting smart inverters have been less studied yet. This paper explores potential malware attacks targeting smart inverters and proposes a deep transfer-learning (DTL)-based malware detection framework for smart inverters. The proposed DTL method can significantly reduce development time and efforts for an artificial intelligence-based malware detection algorithm while improving detection accuracy. The experimental result shows that the proposed method achieves 98% of firmware malware detection accuracy. Furthermore, this approach will be transformative to other smart grid devices enabling seamless firmware update.
- Research Organization:
- University of Arkansas, Fayetteville, AR (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- DOE Contract Number:
- EE0009026
- OSTI ID:
- 2344999
- Conference Information:
- Journal Name: 2022 IEEE Design Methodologies Conference (DMC)
- Country of Publication:
- United States
- Language:
- English
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