Comprehensive Survey of Machine Learning Techniques for Detecting and Preventing Network Layer DoS Attacks

With the increasing reliance on computer networks in our daily lives, the threat of network layer DoS (Denial of Service) attacks has become more prevalent. Attackers use various techniques to disrupt network services and cause loss of data, revenue, and reputation. Recent development in machine learning approaches have shown promise in prevention and detection of such types of attacks by several orders of magnitude. In this paper a thorough overview of machine learning approaches for detecting and preventing network layer DoS attacks is presented. Firstly, the basics of network layer DoS attacks, their classification, and the impact of these attacks is discussed. Then, different machine learning techniques and the ways in which they can be utilized for attack detection and prevention is explored. Additionally, analysis on the strengths and limitations of each approach, and provide a comparative study of the most relevant works in this field is done. Finally, some obstacles in research and potential avenues for future exploration is presented. in the field of machine learning-based defense mechanisms against network layer DoS attacks is discussed. In this paper a detailed summary of the most up-to-date advancements or developments in machine learning-based defense mechanisms against network layer DoS attacks is shown and serve as a reference for one and all who are involved in this field.