DDos attack detection method based on machine learning

Abstract

This paper introduces a novel machine learning-based approach for the detection of Distributed Denial of Service (DDoS) attacks. The proposed method employs three different classifiers, namely Support Vector Machine (SVM), Random Forest, and Multilayer Perceptron (MLP), to accurately classify network traffic as normal or malicious. The approach incorporates various features extracted from network traffic, such as packet length, packet inter-arrival time, and destination port number, to train the classifiers. The results demonstrate high accuracy rates, with Random Forest outperforming the other classifiers in terms of detection accuracy. This proposed method offers a promising solution for detecting DDoS attacks in real-time and has the potential to be integrated into existing network security systems. The issue of DDoS attacks is becoming increasingly critical, with the proliferation of connected devices and the growing dependence on the Internet. There is an urgent need for advanced techniques to detect and mitigate such attacks. Machine learning approaches have shown great potential in identifying anomalous behavior and detecting DDoS attacks in real-time. The proposed method is a promising step towards achieving this goal. Furthermore, the proposed approach has practical applications in the context of network security. By integrating this method into existing security systems, it will enhance the system's ability to detect and prevent DDoS attacks. The approach can be implemented in different network environments, making it versatile and applicable to a variety of settings. In conclusion, the proposed machine learning-based approach provides a robust and effective solution for the detection of DDoS attacks. It is capable of accurately classifying network traffic as normal or malicious in real-time, and has the potential to enhance the overall security of networking systems.