Randomization effect for enhanced IoT-network intrusion detection systems
The rapid growth of Internet of Things (IoT) devices has introduced significant security challenges, as traditional network intrusion detection systems (NIDS) are often inadequate for IoT environments. This paper presents the effect of randomization on the performance of different machine learn...
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| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | en |
| Published: |
IEEE
2026
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| Subjects: | |
| Online Access: | https://irep.iium.edu.my/128488/7/128488_%20Randomization%20effect%20for%20enhanced.pdf https://irep.iium.edu.my/128488/ https://ieeexplore.ieee.org/document/11474101 |
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| Summary: | The rapid growth of Internet of Things (IoT)
devices has introduced significant security challenges, as
traditional network intrusion detection systems (NIDS) are
often inadequate for IoT environments. This paper presents the
effect of randomization on the performance of different machine
learning-based IoT Network Intrusion Detection Systems (IoTNIDS)
for IoT networks. Combinations of XGBoost, Random
Forest, and CNN + LSTM models, both with and without
randomization, were trained and tested on relevant IoT datasets
to achieve an optimal solution. The findings demonstrate that
the proposed machine learning-based NIDS provides a robust
and efficient solution for IoT security, capable of handling
various types of network intrusions. The performance
comparison across datasets revealed that no single model
performed optimally for all scenarios, emphasizing the
importance of dataset-specific customization in NIDS design.
The incorporation of randomization proved particularly
valuable in enhancing the generalization of CNN+LSTM for
unseen data, aligning with the recent findings in the literature. |
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