Detecting Network Intrusion Anomalies with RNN (LSTM)- Based Deep Learning Models
Mr Yamanappa · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2023
As more and more network devices and services are used, there is a growingneed for security measures as hackers attempt to take down or steal data from target systems. One of the key components of network perimetersecurity isthe intrusion detectionsystem (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 notincorporated 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 networktraffic 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. Keywords: Machine Learning, Deep Learning ,Network Intrusion Detection, Long Short-Term Memory ,Feature Selection