HTTP Flood Detection for Smart Agriculture Using Nginx and Flask

DOI

10.2991/978-94-6239-791-0_2How to use a DOI?

Keywords

HTTP flood; Nginx; Flask; defense; request; attack; agriculture

Abstract

The paper proposes a combined model to identify and prevent HTTP Flood Distributed Denial-of-Service (DDoS) attacks in smart agriculture systems with an Nginx-based filtering layer over a Flask-based monitoring system. The proposed system, in contrast to the traditional rule-based methods, presents a weakly structured anomaly detection model to achieve greater flexibility in response to dynamic traffic scenarios. The experimental analysis involved simulated attack scenarios at different rates of requests. The system was also found to be 92.4 percent accurate in detection, achieved a 37 percent lower average response latency and had a false positive rate of less than 4.8 percent with a high load environment. Comparative analysis with baseline configurations (without mitigation and Nginx-only filtering) shows that there are considerable improvements in request filtering efficiency and system stability. Although the AI/ML aspect is deployed as a lightweight classifier to score anomalies, it is not used to substitute rule-based filtering but to complement it. The suggested solution offers an efficient and scalable defense system that is specific to resource-limited smart agriculture systems.

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  - Kavita Kumavat
AU  - Sanjesh Pawale
PY  - 2026
DA  - 2026/10/09
TI  - HTTP Flood Detection for Smart Agriculture Using Nginx and Flask
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 5
EP  - 13
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_2
DO  - 10.2991/978-94-6239-791-0_2
ID  - Kumavat2026
ER  -