Anomaly Detection in Network Traffic: A Scalable Solution for Real-World Security

DOI

10.2991/978-94-6239-674-6_41How to use a DOI?

Keywords

Anomaly Detection; network traffic; cybersecurity

Abstract

One of the most important elements of network traffic cybersecurity is an anomaly detection algorithm that searches for differences in normal trends that may indicate a cyber threat (such as malware, intrusions, or denial-of-service attacks). Machine learning and deep learning methods are needed to better detect anomalies, which more often than not traditional rule-based systems fail to do with the evolving cyberthreats. This research project discusses some of the approaches employed in network anomaly detection including deep learning schemes, supervised and unsupervised learning, clustering algorithms, and statistical models. It also evaluates the efficacy of these techniques by analyzing their accuracy, precision, memory and the computing efficiency. The current paper contributes to an improved understanding of the ways anomaly detection can be improved to suit real-time usage in modern networks by examining the existing developments and challenges.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shubham Dhiman
AU  - Anshika Tutoo
AU  - Sonam Sharma
PY  - 2026
DA  - 2026/05/28
TI  - Anomaly Detection in Network Traffic: A Scalable Solution for Real-World Security
BT  - Proceedings of the International Conference on Sustainable Computing and Artificial Intelligence (ICSCAI 2025)
PB  - Atlantis Press
SP  - 499
EP  - 510
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-674-6_41
DO  - 10.2991/978-94-6239-674-6_41
ID  - Dhiman2026
ER  -