Machine Learning based Multi-Classification Approach for Enhancing Malware Detection for Risk Mitigation

Authors

  • Shantanu Kumar, Harshavardhan Nerella, Shruti Singh

Keywords:

FaaS; Application development; Cloud Environment; Efficiency.

Abstract

Function as a Service (FaaS) has transformed application development in cloud environments by simplifying the deployment process and letting developers concentrate exclusively on coding, barring the need to worry about infrastructure administration. By utilizing FaaS, developers have the option to decompose their applications into smaller, autonomous functions that are executed in response to events, leading to enhanced efficiency and scalability. The server less computing architecture obviates the necessity of providing and managing servers, enabling developers to expedite the delivery of applications in a more cost-efficient manner. The main aim of this study is to analyse the influence of FaaS on the efficiency of application development in cloud environments. The study employs a systematic process for reviewing the literature. This study performed a thorough review of 28 papers that were published from 2018 to 2024.This systematic literature analysis attempts to thoroughly assess the impact of FaaS, a famous model of server less computing, on the productivity of application development in cloud computing settings. The purpose of the review is to analyse and consolidate current research findings on the implementation and usage of FaaS. It aims to assess the impact of FaaS on several aspects such as the development process, performance metrics, operational requirements, scalability, reliability, and cost-effectiveness. This study demonstrates that FaaS greatly improves application development speed in cloud environments by providing a scalable, event-driven architecture, decreasing the burden of managing infrastructure, and enabling quick deployment of code snippets.

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Published

30.05.2024

How to Cite

Shantanu Kumar. (2024). Machine Learning based Multi-Classification Approach for Enhancing Malware Detection for Risk Mitigation. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3641 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6091

Issue

Section

Research Article