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

• URL: http://arxiv.org/abs/2405.11601v1
• Date: Sun, 19 May 2024 16:10:03 GMT
• Title: How to integrate cloud service, data analytic and machine learning technique to reduce cyber risks associated with the modern cloud based infrastructure
• Authors: Upakar Bhatta,
• Abstract summary: Combination of cloud technology, machine learning, and data visualization techniques allows hybrid enterprise networks to hold massive volumes of data. 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.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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