Detecting Denial of Service Attacks using Machine Learning

Abstract

Distributed Denial of Service (DDoS) attacks continue to pose a severe threat to the availability and integrity of online services. This report explores the application of machine learning techniques as a means of enhancing the detection capabilities against these evolving cyber threats. Beginning with an overview of the DDoS landscape, the report delves into the key objectives of utilizing machine learning, including the identification of relevant features, exploration of supervised and unsupervised learning approaches, and examination of challenges such as imbalanced datasets and adaptive attacks. Through a comprehensive literature review and analysis of real-world case studies, this report evaluates the effectiveness of various machine learning models in mitigating DDoS attacks. Furthermore, it discusses challenges related to imbalanced datasets and adaptive attacks, proposing strategies for improvement. The report concludes with insights into future directions for research and development, emphasizing the need for adaptive and real-time detection mechanisms to combat the ever-changing nature of DDoS threats.