Detecting Network Intrusion Anomalies with RNN (LSTM)- Based Deep Learning Models

As more and more network devices and services are used, there is a growing need for security measures as hackers attempt to take down or steal data from target systems. One of the key components of network perimeter security is the intrusion detection system (IDS), which looks through operating system logs or network traffic packets to identify threats. Although previous research has shown the effectiveness of several machine learning algorithms, relatively few of these studies have made use of the time-series information included in network traffic data. Neural network-based methods have not incorporated category data either. In this paper, we offer models for network intrusion detection based on categorical information using the embedding technique and sequential information using the long short-term memory (LSTM) network. Using the extensive network traffic dataset KDD CUP 99, we have tested the models. The findings of the trial confirm that the suggested strategy improves performance, with a 99.72% binary classification accuracy.