Construction of a network intrusion detection system based on a convolutional neural network and a bidirectional gated recurrent unit with attention mechanism

Abstract

The object of this study is the process of recognizing intrusions in computer networks. Network intrusion detection systems (NIDS) have become an important area of research as they are used to protect computer systems from hacker attacks. Deep learning is becoming increasingly popular for detecting and classifying malicious network traffic, including for building NIDS. In this paper, we propose a network intrusion detection model CNN-BiGRU-Attention based on a time-based approach to deep learning using the attention mechanism. The main goal of the study is to build an effective combined deep learning model that can detect various network cyber threats. A 1D convolutional neural network is implemented to extract high-level representations of intrusion information features. A bidirectional gated recurrent unit (BiGRU) with an attention mechanism for traffic data classification has been designed. The attention mechanism plays a key role in the constructed model as it allows the system to focus only on important aspects of network traffic and allows the model to adapt to new types of threats. The results of the study show that using a combination of CNN and BiGRU with the attention mechanism speeds up and improves the process of classifying network attacks. On the NSL-KDD and UNSW-NB15 training datasets, the model shows an accuracy of 99.81 % and 97.80 %. On the NSL-KDD and UNSW-NB15 test datasets, the model demonstrates 82.16 % and 97.72 % accuracy. The proposed NIDS model will be considered for implementation in a real-time corporate network security system. In general, the results of the study provide a new perspective on improving the performance of NIDS and are quite relevant in terms of using attention mechanisms to classify network traffic