Security Analysis And Data Recovery On Large-Scale Computer Networks

Authors

  • Fauzan Prasetyo Eka Putra Universitas Madura, Indonesia
  • Syirofi Universitas Madura, Indonesia
  • Durrahman Wahid Universitas Madura, Indonesia
  • Abd. Mu’iz Syam Universitas Madura, Indonesia

DOI:

https://doi.org/10.47709/brilliance.v5i1.6276

Keywords:

Security, Data Recovery, Computer Network, Encrytion, Data Backup

Abstract

Large computer networks are now needed to help institutions, businesses, and governments do their work in today's complex and connected digital world. At the same time, the growth of very large and important data sets brings new problems for keeping data safe and quickly getting it back when needed. This research aims to carefully study security plans and data recovery methods that can be used well with large computer networks. This research looks at scientific papers, information technology articles, and related research reports from the last ten years. The main things to do are to use firewalls in layers, use encryption methods like AES and RSA, and use systems to find and block malware by looking at its signatures and actions. We also need to make the physical security of network devices stronger, automatically manage updates, and set up regular data backups along with a plan for recovering from disasters (DRP). The study's results show that a complete security and recovery system that combines different parts can greatly improve how well the system can handle different threats, whether they are technical or not. Working together to prevent cyberattacks and being ready to recover data is very important for keeping information technology services running smoothly. This research gives key suggestions on how to build a network that is not only secure and works well but can also quickly recover if there is an emergency or system problem.

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Published

2025-07-14

How to Cite

Putra, F. P. E., Syirofi, S., Wahid, D., & Syam, A. M. (2025). Security Analysis And Data Recovery On Large-Scale Computer Networks. Brilliance: Research of Artificial Intelligence, 5(1), 384–390. https://doi.org/10.47709/brilliance.v5i1.6276

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