Enhancing Iot Intrusion Detection With Transformer-Based Network Traffic Classification

The fast rise of cyber threats has exposed flaws in traditional intrusion detection systems (IDS), particularly in the Internet of Things (IoT) context. Large language models (LLMs) and other deep learning techniques provide sophisticated skills for recognizing intricate attack patterns, which can enhance IDS capabilities. Using two benchmark datasets, we conducted a comparative analysis of IoT network intrusion detection using encoder-based Transformer models and two baseline deep learning models. Experiments on the NF-BoT-IoT and NF-ToN-IoT datasets showed that the evaluated transformer models (BERT-tiny, Electrasmall, and DistilBERT) performed well in binary and multi-class classification tasks. By utilizing self-attention mechanisms, these models surpass the baseline models in capturing crucial context. According to our experiments, the best binary classification accuracies were 99.38 % and 99.98 % for the NF-BoT-IoT and NF-ToN-IoT datasets, respectively. In multi-class classification, DistilBERT achieved the highest accuracy (84.35 %) on the NF-BoT-IoT dataset, whereas BERT-tiny achieved the highest accuracy ($\mathbf{7 2. 7 \%}$) on the NF-ToN-IoT dataset. For the Baseline models, multi-layer perceptron (MLP) demonstrated competitive binary classification performance with low processing costs, whereas long short term memory (LSTM) struggled with multiclass classification, highlighting Transformer models' superiority for high-dimensional, multi-class data. Our findings validate Transformer models' suitability for sophisticated intrusion detection systems, providing significant improvements in detection accuracy and overall efficiency across complicated attack scenarios in IoT networks.

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