Smart DDoS Protection: AI-Powered Detection and Prevention Across Modern Network Architectures

DOI

10.2991/978-94-6239-768-2_5How to use a DOI?

Keywords

DDoS detection; Gramian Angular Field; CNN-GRU; deep learning; network security; intrusion detection; time-series encoding; edge computing

Abstract

Distributed Denial-of-Service (DDoS) attacks have remained a serious and increasing threat in networked environments. The threat posed by DDoS attacks is further compounded by the evolving nature of network infrastructures. The current paper proposes a model for the detection of DDoS attacks using a combination of Gramian Angular Field Images and a Convolutional Neural Network-Gated Recurrent Unit model. This model shows a high level of classification accuracy of 98.6% in the detection of DDoS attacks by transforming the time-series data into a 2D polar coordinate system using a spatio-temporal fusion. The model’s performance is further shown to be superior to that of other detection models such as k-NN, SVM, Random Forest, and a single-branch CNN in all metrics.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, 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 you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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TY  - CONF
AU  - Kavya Poddar
AU  - T. Sudhakar
AU  - J. Logeshwaran
PY  - 2026
DA  - 2026/09/07
TI  - Smart DDoS Protection: AI-Powered Detection and Prevention Across Modern Network Architectures
BT  - Proceedings of the Third International Conference on Recent Advances in Computing Sciences (RACS 2025)
PB  - Atlantis Press
SP  - 41
EP  - 45
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-768-2_5
DO  - 10.2991/978-94-6239-768-2_5
ID  - Poddar2026
ER  -