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

TL;DR

This paper proposes a federated learning approach using CNNs to detect ransomware attacks in digital substations, transforming binary data into images for improved detection accuracy.

Contribution

It introduces a novel ransomware detection method leveraging federated learning and CNNs, with a unique data transformation technique for enhanced accuracy.

Findings

High detection accuracy achieved

Effective ransomware attack modeling for digital substations

Federated learning enhances privacy and detection performance

Abstract

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.