Anomaly detection system for network transport with machine learning approach

Abstract

The rapid growth of network infrastructures and the increasing volume of data transmitted through them have led to a critical need for efficient and accurate anomaly detection systems in network transport. This paper proposes a novel anomaly detection system that utilizes machine learning techniques to identify abnormal patterns and deviations in network traffic. The proposed system follows a multi-layered approach, starting with the collection of network traffic data from various sources, including routers, switches, and gateways. The data is then preprocessed to extract relevant features and eliminate noise. Feature extraction is carried out using statistical, time-series, and flow-based analysis to capture the inherent characteristics of network communication. Machine learning algorithms, such as neural networks, or in this case, auto-encoders, will be trained to learn the patterns of normal network behavior and subsequently detect deviations from these patterns as anomalies. The system provides alerts and notifications to network administrators, allowing prompt investigation and response to potential security threats or network performance issues. It effectively differentiates between benign and malicious network activities, enabling network administrators to take proactive measures to secure their infrastructure and ensure uninterrupted communication.