Big Data and Deep Learning for Scalable Network Traffic Monitoring and Analysis

V. Lakshmi Chaitanya, M. Sharmila Devi, K. Vyshnavi, U. Deepika, S. Sana Samreen, U. Jayanthi

2025

Abstract

Network traffic analysis is important to assure the security, efficiency and proper management of digital communications. Due to the increased speed and complexity of cyberspace, traditional methods for monitoring network traffic are often difficult to enable threats. Modern network security is based on advanced data analytics techniques that handle large amounts of information to identify potential threats and anomalies. This paper focuses on implementing big data technologies and deep learning models to improve large-scale network traffic analytics. Machine learning techniques and deep learning techniques such as folding fish networks (CNNS) and repeating neural networks (RNNs) can help identify suspicious activity by determining patterns of network traffic. As the internet speed increases and more devices are created on the network, traditional methods become effective for large data flows. Using big data frameworks such as Apache Spark and Hadoop, systems can efficiently process and analyze network traffic in real time. Big data integration in deep learning improves security by quickly capturing and reducing cyber threats such as: This paper examines how these advanced technologies can enhance network security, prevent attacks, and improve overall Internet security. Additionally, the future developments and improvements in network surveillance to ensure a safer and more efficient digital environment. The ultimate goal is to create a system that protects our data, improves online security, and ensures seamless digital interaction.

Paper Citation

in Harvard Style

Chaitanya V., Devi M., Vyshnavi K., Deepika U., Samreen S. and Jayanthi U. (2025). Big Data and Deep Learning for Scalable Network Traffic Monitoring and Analysis. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 538-542. DOI: 10.5220/0013886100004919

in Bibtex Style

@conference{icrdicct`2525,
author={V. Chaitanya and M. Devi and K. Vyshnavi and U. Deepika and S. Samreen and U. Jayanthi},
title={Big Data and Deep Learning for Scalable Network Traffic Monitoring and Analysis},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={538-542},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013886100004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Big Data and Deep Learning for Scalable Network Traffic Monitoring and Analysis
SN - 978-989-758-777-1
AU - Chaitanya V.
AU - Devi M.
AU - Vyshnavi K.
AU - Deepika U.
AU - Samreen S.
AU - Jayanthi U.
PY - 2025
SP - 538
EP - 542
DO - 10.5220/0013886100004919
PB - SciTePress