How to integrate cloud service, data analytic and machine learning technique to reduce cyber risks associated with the modern cloud based infrastructure

TL;DR

This paper explores integrating cloud services, data analytics, and machine learning to enhance security and reduce cyber risks in modern cloud infrastructures, demonstrating practical applications with real datasets.

Contribution

It presents a framework combining cloud, machine learning, and data visualization techniques for cyber risk detection and reduction in cloud environments.

Findings

Machine learning classifiers can predict network behavior metrics.

Data visualization aids in understanding network traffic patterns.

Integrated approach improves security threat detection.

Abstract

The combination of cloud technology, machine learning, and data visualization techniques allows hybrid enterprise networks to hold massive volumes of data and provide employees and customers easy access to these cloud data. These massive collections of complex data sets are facing security challenges. While cloud platforms are more vulnerable to security threats and traditional security technologies are unable to cope with the rapid data explosion in cloud platforms, machine learning powered security solutions and data visualization techniques are playing instrumental roles in detecting security threat, data breaches, and automatic finding software vulnerabilities. The purpose of this paper is to present some of the widely used cloud services, machine learning techniques and data visualization approach and demonstrate how to integrate cloud service, data analytic and machine learning techniques that can be used to detect and reduce cyber risks associated with the modern cloud based infrastructure. In this paper I applied the machine learning supervised classifier to design a model based on well-known UNSW-NB15 dataset to predict the network behavior metrics and demonstrated how data analytics techniques can be integrated to visualize network traffics.