Attack detection in internet of things networks with deep learning using deep transfer learning method - Riki Abdillah Hasanuddin; Muhammad Subali (Computer Science and Information Technologies, 2025)
Cybersecurity becomes a crucial part within the information management framework of internet of things (IoT) device networks. The large-scale distribution of IoT networks and the complexity of communication protocols used are contributing factors to the widespread vulnerabilities of IoT devices. The implementation of transfer learning models in deep learning can achieve optimal performance faster than traditional machine learning models, as they leverage knowledge from previous models that already understand these features. Base model was built using the 1-dimension convolutional neural network (1D-CNN) method, using training and test data from the source domain dataset. Model 1 was constructed using the same method as base model. The test and training data used for model 1 were from the target domain dataset. This model successfully detected known attacks at a rate of 99.352%, but did not perform well in detecting unknown attacks, with an accuracy of 84.645%. Model 2 is an enhancement of model 1, incorporating transfer learning from the base model. Its results significantly improved compared to model 1 testing. Model 2 has an accuracy and precision rate of 98.86% and 99.17 %, respectively, allowing it to detect previously unknown attacks. Even with a slight decrease in normal detection, most attacks can still be detected.