Combined Deep Learning Approaches for Intrusion Detection Systems

Abstract

Cybersecurity has become increasingly important because of the widespread use of data and its enormous global storage. Hackers and other invaders always want to breach data security by interfering with network traffic. The breaches must be stopped by several tools, such as firewalls. Other solutions, such as intrusion detection systems (IDSs), may detect network intrusions effectively. In this paper, we introduce a hybrid technique (CNN-LSTM) that combines the convolutional neural network (CNN) with long short-term memory (LSTM), a modified version of the recurrent neural network (RNN). The model is tested using the CSE-CIC-IDS2018 dataset. Both CNN and LSTM were individually applied to the datasets, and the results are compared with our hybrid CNN-LSTM model. The hybrid CNN-LSTM model demonstrated higher accuracy (99%) during both training and validation processes compared to individual models; the accuracy of the CNN model is 92% and the accuracy of the LSTM is 93.5%. The outcomes validate the usefulness and effectiveness of the hybridizing model.