Detection of ransomware attacks using federated learning based on the CNN model

Computing is still under a significant threat from ransomware, which necessitates prompt action to prevent it. Ransomware attacks can have a negative impact on how smart grids, particularly digital substations. In addition to examining a ransomware detection method using artificial intelligence (AI)...

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Bibliographic Details
Published inarXiv.org
Main Authors Hong-Nhung Nguyen, Ha-Thanh Nguyen, Lescos, Damien
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.05.2024
Subjects
Artificial intelligence
Artificial neural networks
Binary data
Digital imaging
Federated learning
Machine learning
Ransomware
Smart grid
Substations
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Summary:Computing is still under a significant threat from ransomware, which necessitates prompt action to prevent it. Ransomware attacks can have a negative impact on how smart grids, particularly digital substations. In addition to examining a ransomware detection method using artificial intelligence (AI), this paper offers a ransomware attack modeling technique that targets the disrupted operation of a digital substation. The first, binary data is transformed into image data and fed into the convolution neural network model using federated learning. The experimental findings demonstrate that the suggested technique detects ransomware with a high accuracy rate.
ISSN:2331-8422