Real-Time Threat Detection Using Network Flow Analysis and LSTM Networks

Abstract

The increasing volume and sophistication of cyberattacks demand advanced techniques for real-time threat detection in network environments. Traditional signature-based intrusion detection systems often fail to detect novel or evolving threats. This paper presents a deep learning approach that leverages network flow data and Long Short-Term Memory (LSTM) networks for early and accurate anomaly detection. Using the CICIDS2017 dataset, which includes benign and malicious traffic patterns across various attack vectors (e.g., DDoS, PortScan, BotNet), we construct a time-series representation of flow statistics including packet counts, byte counts, and time deltas. The LSTM model is trained to recognize normal traffic patterns and flag deviations as potential threats. Our model achieves a detection accuracy of 94.5% with a low false-positive rate of 3.1%. We compare its performance with classical machine learning models such as Random Forest and Support Vector Machines, noting superior recall and faster detection latency with LSTM. Furthermore, the system supports online inference, making it suitable for deployment in high-throughput environments. The paper discusses limitations, including model interpretability and handling encrypted traffic. By combining temporal awareness and behavioral modeling, this work contributes to the development of intelligent, adaptive intrusion detection systems that can be deployed in modern network security architectures.