DDoS attack detection mechanism in the application layer using user features

Abstract

DDoS attacks are one of the most damaging computer aggressions of recent times. Attackers send large number of requests to saturate a victim machine and it stops providing its services to legitimate users. In general attacks are directed to the network layer and the application layer, the latter has been increasing due mainly to its easy execution and difficult detection. The present work proposes a low cost detection approach that uses the characteristics of the Web User for the detection of attacks. To do this, the features are extracted in real time using functions designed in PHP and JavaScript. They are evaluated by an order 1 classifier to differentiate a real user from a DDoS attack. A real user is identified by making requests interacting with the computer system, while DDoS attacks are requests sent by robots to overload the system with indiscriminate requests. The tests were executed on a computer system using requests from real users and attacks using the LOIC, OWASP and GoldenEye tools. The results show that the proposed method has a detection efficiency of 100%, and that the characteristics of the web user allow to differentiate between a real user and a robot.