Machine learning algorithms are used to solve complex problems in many fields. These algorithms are also applied for detection of DDoS attacks, and it has been found that they are better than signature-based detection techniques. These ML-based classifiers can be trained to determine the abnormal
behaviour of the network traffic with more accuracy. Some commonly used classifiers based on machine learning are Support Vector Machine (SVM), Hidden Markov Model (HMM), Decision Tree (J48), Naive Bayes, Logistic regression, Random Trees, Binary Bat algorithm, Random forest, and K-nearest neighbour (KNN). This is a comparison of Machine learning approaces for DDoS attack detection.
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Singh, J., & Behal, S. (2020). Detection and mitigation of DDoS attacks in SDN: A comprehensive review, research challenges and future directions. Computer Science Review, 37, 100279.