Enhancing IoT Network Defense: A Comparative Study of Machine Learning Algorithms for Attack Classification
Document Type
Conference Proceeding
Publication Date
10-19-2024
School
Computing Sciences and Computer Engineering
Abstract
As the Internet of Things (IoT) continues to expand rapidly, securing these interconnected devices and networks from cyber threats has become a critical challenge. This research investigates the application of machine learning techniques for accurately classifying IoT network traffic data to discriminate between benign activities and various types of cyber-attacks targeting IoT systems. We propose a program that employs multiple machine learning algorithms, including Decision Tree, Logistic Regression, Naive Bayes, and Random Forest, trained on a comprehensive IoT network traffic dataset the CICIoTDataset2023. Through extensive experiments, we evaluate the performance of these classification models in detecting different IoT attack categories such as web-based attacks, spoofing, denial-of-service, Mirai, reconnaissance, distributed denial-of-service, and brute force attacks. Our results demonstrate the efficacy of machine learning approaches, with the Random Forest algorithm emerging as the top performer, achieving an overall accuracy of 98.41%. We also address challenges like class imbalance through hybrid sampling techniques and implement strategies like regularization and hyperparameter tuning to mitigate overfitting and enhance model generalization. Additionally, we conduct a performance analysis of the classification models on different IoT attack categories to gain insights into their specific strengths and weaknesses. By leveraging machine learning for accurate IoT attack classification, this research contributes to developing robust security solutions that can proactively identify and mitigate cyber threats, enabling a more secure IoT ecosystem. The findings pave the way for safeguarding interconnected devices, protecting user privacy, and fostering confidence in the widespread adoption of IoT technologies.
Publication Title
Communications in Computer and Information Science
Volume
2244 CCIS
First Page
55
Last Page
64
Recommended Citation
McNair, A.,
Precious-Esue, D.,
Newson, S.,
Rahimi, N.
(2024). Enhancing IoT Network Defense: A Comparative Study of Machine Learning Algorithms for Attack Classification. Communications in Computer and Information Science, 2244 CCIS, 55-64.
Available at: https://aquila.usm.edu/fac_pubs/21852
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