Core Concepts
Proposing the CyberWise Predictor framework for predicting and assessing cybersecurity risks in microservice architectures using deep learning-based natural language processing models.
Abstract
The article introduces the CyberWise Predictor framework, focusing on cybersecurity risk assessment in microservice architectures. It addresses challenges in security risk assessment and proposes a solution using deep learning-based models. The content is structured as follows:
Introduction to Microservice Architectures
Challenges of Microservices in Cybersecurity
Proposed Solution: CyberWise Predictor Framework
Taxonomy of Microservice Vulnerabilities
Architecture of CyberWise Predictor
Data Imputation with Deep Learning Models
Experimental Results and Model Performance Evaluation
Stats
Our framework achieves an average accuracy of 92% in predicting vulnerability metrics.
A total of 2395 vulnerabilities were detected in the Sock Shop benchmark application.
RoBERTa model demonstrated high accuracy rates ranging from 0.896 to 0.944 across different CVSS metrics.