Optimizing Machine Learning Models for IoT-Based DDoS Attack Detection through Hyper parameter Tuning

Authors

  • A.Priyadharshini Ph.D. Research Scholar, Department of Computer Science, Rathinam College of Arts and Science, Coimbatore – 641021
  • S.Dhinakaran Assistant Professor, Department of Computer Science, Rathinam College of Arts and Science, Coimbatore – 641021

Keywords:

DDos attack, Grid search, Hyperparameter optimization, Internet of things, Machine learning, Network security.

Abstract

The proliferation of Internet of Things (IoT) devices has escalated the complexities and frequencies of Distributed Denial of Service (DDoS) attacks, making traditional detection mechanisms inadequate. This paper explores the enhancement of machine learning (ML) models specifically tuned for IoT environments using systematic hyperparameter optimization via grid search. By tailoring the learning processes and configurations of models such as Random Forest, XGBoost, and Support Vector Machines, the study achieves superior detection rates, reduced false positives, and improved computational efficiency. The findings suggest that precise hyperparameter tuning is crucial for adapting DDoS detection systems to the unique characteristics of IoT networks, thereby offering robust defenses against evolving cyber threats.

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Published

2024-09-10

How to Cite

A.Priyadharshini, & S.Dhinakaran. (2024). Optimizing Machine Learning Models for IoT-Based DDoS Attack Detection through Hyper parameter Tuning. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 31–40. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1222

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