Detection of HTTP DDoS Attacks Using NFStream and TensorFlow

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.

Automated summary

This paper presents the implementation of nfstream, an open source network data analysis tool and machine learning model based on TensorFlow, for detecting HTTP attacks. HTTP attacks are a common and significant security threat to networked systems. The proposed approach utilizes TensorFlow's machine learning capabilities to detect these attacks. The paper also discusses the collection and analysis of network traffic data using nfstream, which provides detailed analysis of network traffic flows. The collected data is pre-processed and transformed into vectors, which are then used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow achieves high accuracy in detecting HTTP attacks, with minimal false positives.