NS-3-Based Modeling and Detection of DDoS Attacks in Internet of Things Networks

Ali Kurji

doi:10.51173/eetj.v2i2.25

Authors

Ali Kurji Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

DOI:

https://doi.org/10.51173/eetj.v2i2.25

Keywords:

IoT Security, DDoS Attack, NS-3 Simulator, Traffic Analysis, Machine Learning

Abstract

The Internet of Things has grown quickly in the last few years, adding new features to everyday devices while also making those systems more vulnerable to more advanced Distributed Denial-of-Service attacks. Because of this two-sided growth, the current study uses the NS-3 simulator for a detailed traffic analysis that can find and stop DDoS attacks. The experiment used several machine-learning classifiers, including Random Forest, Support Vector Machine, and Long Short-Term Memory, to see how fast and accurate they were. The LSTM (Long Short-Term Memory) model was able to find DDoS attacks in the simulated IoT environment with 98.5% accuracy. This was mostly because it could accurately capture temporal patterns and dependencies in sequential network traffic data. The strategy as a whole lays out a set of security rules that should work well across large IoT networks.

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