Detection of HTTP DDoS Attacks Using NFStream and TensorFlow

Abstract

This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives.

Keywords

• TensorFlow
• NFStream
• machine learning
• HTTP DDoS