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

Aug 4, 2026

Upload by Noah

Cloud platforms enable hybrid enterprises to store and access massive, complex datasets, but this scale also intensifies security threats and creates gaps that traditional defenses struggle to address. The paper presents widely used cloud services, data-visualization approaches, and machine-learning techniques, showing how to integrate them to detect data breaches, identify vulnerabilities, and reduce cyber risks in modern cloud-based infrastructure. It demonstrates a supervised machine-learning model using the UNSW-NB15 dataset to predict network behavior metrics and uses data analytics to visualize network traffic anomalies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Proposed Idea

Problem Statement

Pre-Reqquisite Knowledge

Cloud Computing

Big Data Analytics

How to integrate cloud service, data analytic and machine learning technique to reduce cyber risks associated with the modern cloud based infrastructure Upakar Bhatta Marymount University Arlington, VA, USA [upakarb@gmail.com](upakarb@gmail.com) In today’s dynamic and competitive digital era, companies are leveraging cloud technology, machine learning, and data visualization techniques to reinvent their business processes. 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 wellknown UNSW-NB15 dataset to predict the network behavior metrics and demonstrated how data analytics techniques can be integrated to visualize network traffics. Keywords: Cloud Services, Data Visualization, Machine Learning Approach for Anomaly Detection. I. INTRODUCTION Throughout the last decade, the numbers of malicious incidents have dramatically increased causing a significant impact on the organization infrastructure [1] . Cyber-attacks and security threats have dramatically increased in emerging technologies such as Cloud and Internet of Things (IoT) . During the Covid-19 pandemic, IT professionals see an increase in cyberattacks such as distributed denial-of-service (DDoS) that targets normal network traffic by injecting malicious network traffic and thus disrupting the organization’s system. Several research have been done previously regarding how to monitor and predict network attacks using a machine learning approach to reduce cyber-attacks and security threats. The enterprise security analysis has been highlighted [2] . The investigation of a feasibility study of applying machine learning techniques and overview of state of art machine learning technique for network and cloud security has been outlined [3] . However, previous research doesn’t provide enough detail regarding implementation of machine learning technique to predicate malicious activates. This research study explores the cloud services, data visualization tools, and machine learning techniques and demonstrates these services can be applied to select the network parameter to proactively detect and predict the various types of network attacks. A. Proposed Idea The purpose of this research study is to explore cloud services, machine learning and data visualization techniques and learn how to integrate cloud visualization and machine learning capable service to visualize network anomaly traffic. B. Problem Statement Rodney Alexander highlighted the enterprise security analysis in his paper “Using Linear Regression Analysis and Defense in Depth to Protect Data Breach and Network attacks during the Global Corona Pandemic” [3] . According to Thales Security, 67 percent of enterprises across the world have at least one incident of a major data breach or network attack annually [4] . The general problem is to understand the challenge of machine learning implementation. The specific problem is to understand how effective cloud

FAQs

Why do modern cloud infrastructures create higher cyber risk?

Cloud platforms face security threats that can outpace traditional security technologies due to rapid growth of complex data and infrastructure scale.

What integration does the paper propose to reduce cyber risks?

It integrates cloud services with machine-learning techniques and data-visualization tools to detect, predict, and visualize network attacks and vulnerabilities.

How is machine learning applied in the study?

The study uses a supervised classifier built on the UNSW-NB15 dataset to predict network behavior metrics and support anomaly-focused analysis.

Aug 4, 2026

Upload by Noah