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Ransomware Attack Modeling and Artificial Intelligence-Based Ransomware Detection for Digital Substations

Conference · · 2021 6th IEEE Workshop on the Electronic Grid (eGRID)
 [1];  [2];  [2];  [3];  [4];  [4]
  1. Texas A&M University-Kingsville, TX (United States); Texas A&M University-Kingsville
  2. Texas A&M University-Kingsville, TX (United States)
  3. University of Texas at Austin, TX (United States)
  4. Electrotechnology Research Institute, Changwon (South Korea)
+ Show Author Affiliations

Ransomware has become a serious threat to the current computing world, requiring immediate attention to prevent it. Ransomware attacks can also have disruptive impacts on operation of smart grids including digital substations. This paper provides a ransomware attack modeling method targeting disruptive operation of a digital substation and investigates an artificial intelligence (AI)-based ransomware detection approach. The proposed ransomware file detection model is designed by a convolutional neural network (CNN) using 2-D grayscale image files converted from binary files. Here, the experimental results show that the proposed method achieves 96.22% of ransomware detection accuracy.

Research Organization:
University of Arkansas, Fayetteville, AR (United States)
Sponsoring Organization:
USDOE; National Science Foundation (NSF)
DOE Contract Number:
EE0009026
OSTI ID:
2344969
Journal Information:
2021 6th IEEE Workshop on the Electronic Grid (eGRID), Journal Name: 2021 6th IEEE Workshop on the Electronic Grid (eGRID)
Country of Publication:
United States
Language:
English

References (5)

Blockchain-Enabled Security Module for Transforming Conventional Inverters toward Firmware Security-Enhanced Smart Inverters conference October 2021
Machine Learning-Based Detection of Ransomware Using SDN
  • Cusack, Greg; Michel, Oliver; Keller, Eric
  • Proceedings of the 2018 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization https://doi.org/10.1145/3180465.3180467
conference March 2018
Classification of ransomware families with machine learning based onN-gram of opcodes journal January 2019
Malware images conference July 2011
Ransomware Detection using Random Forest Technique journal December 2020

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Related Subjects

artificial intelligence
attack modeling
convolutional neural network
cybersecurity
digital substation
ransomware