Detection of Attack Behaviors in IoT Networks Using Temporal Convolutional Networks (TCN)
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Abstract
This research aims to (1) develop a framework for detecting attack behaviors in IoT networks using the Temporal Convolutional Networks (TCN) model and (2) evaluate the performance of the TCN model by comparing it with RNN, LSTM, GRU, and DNN models. The experiment was conducted over a 12-week period using Python as the primary programming language. A synthetic dataset was utilized, simulating Denial-of-Service (DoS) and Man-in-the-Middle (MITM) cyberattacks. The research process involved data transformation and preprocessing, followed by training the models using TensorFlow/Keras on Google Colab. The experimental results revealed that the TCN model achieved a high recall score (0.9986), indicating its ability to detect nearly all attack events. However, its accuracy (≈0.50) and precision (0.4978) were low, leading to a high false positive rate. Additionally, the loss values exhibited significant fluctuations, reflecting the model's instability in learning the data effectively. In comparison, the RNN model provided balanced results, LSTM and GRU demonstrated high efficiency in handling complex data, and DNN achieved the highest accuracy. These findings suggest that while the
TCN model performs well in detecting attack behaviors in terms of recall, it requires further improvements to enhance precision and accuracy. To address these issues, improving dataset quality, applying hyperparameter tuning, implementing regularization techniques to reduce overfitting, and testing the framework with real-world datasets are recommended. These improvements will contribute to a more reliable and effective security system for IoT networks.
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