Machine Learning framework for Information Security Management in Big Data Applications

Abstract

Big data has become an integral part of modern businesses, but its management and protection present numerous challenges, such as securing sensitive information from unauthorized access, preventing data breaches, and ensuring data integrity. This work investigated applying a machine learning (ML) approach to tackling the challenges of information security and management in big data environments. We present an ML framework that leverages a supervised learning strategy to detect anomalies, classify big data, and predict potential security threats. We also investigate the implementation of this framework and its potential benefits, such as reducing false positives and improving detection rates. Our experimental analysis in public datasets demonstrates the effectiveness of our approach in improving information security and management in big data environments.