Distributed Denial of Service (DDoS) Attacks Detection Using Machine Learning Prototype

The Distributed Denial of Service (DDoS) attacks affect the availability of Web services for an indeterminate period of time, flooding the company's servers with fraudulent requests and denying requests from legitimate users, generating economic losses by unavailable rendered services. Therefore, the aim of this paper is to show the process of detection prototype DDoS attacks using a supervised learning model by Support Vector Machines (SVM), which captures network traffic, filters HTTP headers, normalizes the data on the basis of the operational variables: rate of false positives, rate of false negatives, rate of classification and then sends the information to corresponding SVM's training and testing sets. The results show that the proposed DDoS SVM prototype has high detection accuracy (99 %) decrease of the false positives and false negatives rates compared to conventional detection models.