An inventive network intrusion detection system: Composite deep learning CNN-LSTM model

The creation of efficient network intrusion detection systems (NIDS) has become vital with the rising occurrence of network intrusions. In this research, we introduce an innovative NIDS method that integrates the strengths of convolutional neural network (CNN) and long short-term memory (LSTM) mechanisms to examine the network traffic data characteristics. We employ the UNSW-NB15 dataset, which showcases a varied distribution of patterns, including a notable imbalance between the size of the training and testing sets. Unlike conventional machine learning methods, which frequently face challenges with restricted feature sets and reduced accuracy, our proposed model addresses these shortcomings. Current models applied to this dataset generally necessitate manual feature selection and extraction, which can be less accurate, labor-intensive and time-consuming. Conversely, our model attains better performance in binary classification by harnessing the benefits of combined CNN and LSTM models. By conducting thorough experiments and evaluations with advanced deep learning models, we showcase the superiority and the efficacy of the proposed approach. The obtained results emphasize the promise of integrating CNN and LSTM in order to improve network intrusion detection.