Open AccessDDoS detection and prevention based on artificial intelligence techniques
Author(s) -
Dragoș Glăvan
Publication year - 2019
Publication title -
scientific bulletin
Language(s) - English
Resource type - Journals
eISSN - 2392-8956
pISSN - 1454-864X
DOI - 10.21279/1454-864x-19-i1-018
Subject(s) - denial of service attack , trinoo , computer science , application layer ddos attack , computer security , the internet , artificial intelligence , naive bayes classifier , cloud computing , world wide web , support vector machine , operating system
Distributed Denial of Service (DDoS) attacks have been the major threats for the Internet and can bring great loss to companies and governments. With the development of emerging technologies, such as cloud computing, Internet of Things (IoT), artificial intelligence techniques, attackers can launch a huge volume of DDoS attacks with a lower cost, and it is much harder to detect and prevent DDoS attacks, because DDoS traffic is similar to normal traffic. Some artificial intelligence techniques like machine learning algorithms have been used to classify DDoS attack traffic and detect DDoS attacks, such as Naive Bayes and Random forest tree. In the paper, we survey on the latest progress on the DDoS attack detection using artificial intelligence techniques and give recommendations on artificial intelligence techniques to be used in DDoS attack detection and prevention.