Advancing Network Security: Developing a Deep Learning- Based Intrusion Detection System

Intrusion Detection Systems (IDS) are essential for protecting network infrastructures against unauthorized access and malicious activities. They act as an essential line of defense, continuously monitoring network traffic and identifying potential security threats. With the emergence of deep learning architectures, a promising approach has been presented to tackle the challenges associated with intrusion detection more effectively. These architectures leverage the power of neural networks to automatically learn patterns and anomalies within network traffic data, leading to enhanced anomaly detection capabilities. This study focuses on evaluating the effectiveness of three deep learning models - Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) - in classifying network traffic to identify potential intrusions. The paper illustrates the outstanding accuracy of these models, with accuracies of 99.97% for GRU, 99.95% for LSTM, and 99.95% for CNN, through intensive testing and assessment. These high accuracy rates in accurately detecting anomalous behaviors within network traffic data highlight the capabilities and effectiveness of deep learning models in intrusion detection.