Smart IoT Security: Lightweight Machine Learning Techniques for Multi-Class Attack Detection in IoT Networks

• URL: http://arxiv.org/abs/2502.04057v3
• Date: Wed, 09 Jul 2025 21:02:16 GMT
• Title: Smart IoT Security: Lightweight Machine Learning Techniques for Multi-Class Attack Detection in IoT Networks
• Authors: Shahran Rahman Alve, Muhammad Zawad Mahmud, Samiha Islam, Md. Asaduzzaman Chowdhury, Jahirul Islam,
• Abstract summary: The Internet of Things (IoT) is expanding at an accelerated pace, making it critical to have secure networks to mitigate a variety of cyber threats.<n>This study addresses the limitation of multi-class attack detection of IoT devices and presents new machine learning-based lightweight ensemble methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Internet of Things (IoT) is expanding at an accelerated pace, making it critical to have secure networks to mitigate a variety of cyber threats. This study addresses the limitation of multi-class attack detection of IoT devices and presents new machine learning-based lightweight ensemble methods that exploit its strong machine learning framework. We used a dataset entitled CICIoT 2023, which has a total of 34 different attack types categorized into 10 categories, and methodically assessed the performance of a substantial array of current machine learning techniques in our goal to identify the best-performing algorithmic choice for IoT application protection. In this work, we focus on ML classifier-based methods to address the biocharges presented by the difficult and heterogeneous properties of the attack vectors in IoT ecosystems. The best-performing method was the Decision Tree, achieving 99.56% accuracy and 99.62% F1, indicating this model is capable of detecting threats accurately and reliably. The Random Forest model also performed nearly as well, with an accuracy of 98.22% and an F1 score of 98.24%, indicating that ML methods excel in a scenario of high-dimensional data. These findings emphasize the promise of integrating ML classifiers into the protective defenses of IoT devices and provide motivations for pursuing subsequent studies towards scalable, keystroke-based attack detection frameworks. We think that our approach offers a new avenue for constructing complex machine learning algorithms for low-resource IoT devices that strike a balance between accuracy requirements and time efficiency. In summary, these contributions expand and enhance the knowledge of the current IoT security literature, establishing a solid baseline and framework for smart, adaptive security to be used in IoT environments.

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