Efficient RNN Models for IOT Intrusion Detection System

Abstract

Due to the growing number of network devices, traffics and services, designing robust Intrusion Detection System (IDS) become a crucial need in the face of complex and various network attacks as a protective measure from hackers and cybercriminals. However, the traditional Machine Learning (ML) approach shows success in many research topics but with the increase in the amount of data and the diversification of network threats methods, it seems to lack reliability and accuracy. Therefore, considering a large amount of real-world cyber traffic, Deep Learning (DL) may be able to extract big data features more effectively. In this paper, we suggest an intrusion detection system for the Internet of Things (IoT) network based on Deep Learning to recognize various assault types for both binary and multiclass classification using two variants of Recurrent Neural Network (RNN) models long short-term memory (LSTM) and Bidirectional LSTM (BiLSTM). We have experimented the models with CSE-CIC-IDS2018, which is the newest comprehensive network traffic dataset. Accuracy, precision, recall, and F1 score are a few performance criteria where the suggested approach clearly excels. After comparison, we can infer that Bi-directional LSTM outperforms LSTM and other existing efforts in the literature. The accuracy of the experimental results was high, coming in at 98.62%.