Detecting Distributed Denial of Service Attacks using Machine Learning

Abstract

Distributed Denial of Service Attack (DDoS) is the most dangerous attack in the field of network security. DDoS attacks halt normal functionality if critical services of various online operations. Systems under DDoS attacks remain occupied with false requests rather than providing services to legitimate users. These attacks are increasing day by day and have become more and more sophisticated with increasingly more complex patterns. So, it has become difficult to detect these attacks and secure online services from these attacks. Whether it is a small non-profit or a huge multinational organization, online services, emails, websites, anything that faces the internet can be slowed or completely stopped by a DDoS attack. The economic impact of DDoS attacks is substantial, especially at a time when we rely on web applications more and more often. That is why, it is essential to be able to detect such threats early and therefore react before significant financial losses.