Evaluating the performance of machine learning techniques in detecting LDoS attacks in SDNs

Abstract

SDNs are still not mature enough, especially in terms of security, and can easily become a prime target for many attacks such as DoS attacks that reduce or block network services and make them unavailable to users, or they may also be a gateway to other attacks.
In this article, we present an evaluation of a set of machine learning algorithms in detecting LDoS attacks in SDNs, where cybersecurity systems can analyze and learn patterns to help prevent similar attacks and respond to changing behavior. This can help cybersecurity research teams be more proactive in preventing threats and responding to active attacks in real time.