INVESTIGATION OF DDoS ATTACKS DETECTION USING MACHINE LEARNING
The article presents a comparison of algorithms for detecting network attacks such as DDoS attacks (denial of service) for various data storage, processing and transmission services over the Internet. Particular attention is paid to the application of machine learning algorithms such as the Gaussian mixed model for expectations maximization (GMM-EM), linear regression (LR), SVM (support vector machine) (with linear, RBF (radial basis function) or polynomial kernels), Decision Tree algorithms, Naive Bayes [4] and Random Forest to detect this type of attacks. At the end of the article, the machine learning algorithms listed above are evaluated and their performance is carefully compared. All experimental results show that more than 99.7% of the two types of DOS attacks are successfully detected. This approach does not reduce performance and can be easily extended to broader DOS attacks.