Improving IoT Intrusion Detection Through SMOTE-Based Oversampling and Extended Multi-Model Evaluation on Side-Channel Power Data
arXiv:2606.00161v1 Announce Type: cross Abstract: The detection of intrusions in IoT-based networks poses challenges that cannot be overcome using traditional machine learning methods. Perhaps the biggest of them is related to the presence of a class imbalance in the side-channel dataset, where the number of samples in the normal class compared to the attacks can reach a ratio of 75,964 to 1. Such an aspect is addressed by Dominguez et al. through the proof of concept of power-based intrusion detection. Unfortunately, neither the authors attempt to cope with the problem of imbalance nor do the
The proliferation of IoT devices and increasing sophistication of cyber threats necessitate advanced intrusion detection methods, making solutions like this paper's proposal highly relevant.
This research is crucial for securing critical infrastructure and vast networks of IoT devices from cyberattacks, which can have significant economic and security implications.
The proposed method improves intrusion detection in IoT networks by addressing severe class imbalance, potentially leading to more robust and reliable security systems.
- · IoT device manufacturers
- · Cybersecurity firms
- · Critical infrastructure operators
- · Cybercriminals
- · Organizations with vulnerable IoT networks
Improved security postures for IoT deployments across various sectors.
Reduced incidence of IoT-related cyberattacks and data breaches.
Increased public and industry trust in IoT technologies, fostering wider adoption and innovation.
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