Detecting Privacy Attacks in IoT Network using Deep Learning models
D R Janardhana, A P Manu, Vinod Kumar · 2023
In this digital era, usage of devices has increased, and more devices are connected to the internet. Smart things allow data collection to enhance the performance of the product. The collection of these data sometimes may lead to privacy and security threats when not implemented correctly in the network. In this paper, we propose Deep Learning (DL) and Machine Learning (ML) models to detect privacy attacks in IoT networks. Deep Learning (DL) and Machine learning (ML) models are designed to detect privacy attacks on UNSW-NB15 Network Dataset. This work considers only privacy attacks namely Exploits, Reconnaissance, and Analysis in the mentioned dataset. Performance evaluation of implemented models conducted with other models in that Convolutional Neural Network (CNN) performed better with high accuracy compared to Recurrent Neural Network (RNN). Also, CNN gave better performance accuracy than traditional classifier machine learning models like SVM and Decision tree (DT).