Machine Learning Techniques for Intrusion Detection in Software-Defined Networks

Introducing Software-Defined Networks opens a greater challenge to the researchers around us in detecting and mitigating cyber-attacks. Traditional intrusion detection systems have outlived their usefulness in such a dynamic and flexible network environment. In this regard, intrusion detection in SDNs, ranging from conventional to machine learning, has been applied widely for this issue. Machine Learning algorithms can quickly parse through massive amounts of data and spot trends or anomalies that could signal an intrusion attempt. For example, anomaly detection techniques train the ML model with normal network usage profiles, and any variation from the norm is treated as a potential threat. This can help enhance intrusion detection's significance and speed compared to traditional methods. An alternative method is classifying network traffic into legit or intrusion by ML models. They are trained on URLs with labels that tell them whether the website is malware, and they will be able to detect infections within milliseconds. In addition, ML techniques can adapt and evolve to new attack vectors, thus making them more robust against unknown threats. Additionally, they can be used alongside other security systems for a more robust and multilayered defense mechanism.