Smart Shield: Machine Learning-Driven Anomaly Detection for DDoS Intrusions in IoT Networks

DOI

10.2991/978-94-6463-805-9_31How to use a DOI?

Keywords

IoT Security; Machine Learning; DDoS Detection; Anomaly Detection; Network Simulation; NS-3

Abstract

Significant issues with identity management, access control, and data and network security arise from the extensive cross-sector integration of IoT. Making sure the components of IoT are secure is essential since it is becoming a target for cyberattacks like malware and DDoS. IoT systems deal with sensitive data, and since they have limited computational and energy resources, it is essential to guarantee confidentiality and regulated access. It is frequently impractical to use traditional security measures like asymmetric encryption or digital certificates. While taking into account particular limitations, this research aims to create effective machine learning techniques for identifying malevolent intrusions and averting data breaches in networked IoT systems. This paper offers a thorough investigation of anomaly detection for Distributed Denial of Service (DDoS) attacks in networks connected to the Internet of Things. The literature that has already been written about intrusion detection systems, IoT security, and pertinent simulation tools is reviewed. The study suggests a machine learning-based approach for identifying DDoS attacks and assesses its effectiveness through network simulation with NS-3. The findings show that the suggested method is successful in spotting unusual network activity that could be a sign of DDoS attacks. By reducing the impact of cyber threats, our work helps to improve the security of IoT environments.

Copyright

© 2025 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  - Sarah Boualam
AU  - Walid Kadri
AU  - Mohamed Aridj
PY  - 2025
DA  - 2025/08/05
TI  - Smart Shield: Machine Learning-Driven Anomaly Detection for DDoS Intrusions in IoT Networks
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 277
EP  - 287
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_31
DO  - 10.2991/978-94-6463-805-9_31
ID  - Boualam2025
ER  -