DDoS detection and prevention based on artificial intelligence techniques

Dragoș Glăvan
<span title="">2019</span> <i title="Naval Academy Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/267jnebqbbf7newhevyxawbqea" style="color: black;">Scientific Bulletin of Naval Academy</a> </i> &nbsp;
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
more &raquo; ... ke 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21279/1454-864x-19-i1-018">doi:10.21279/1454-864x-19-i1-018</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/7e6681b8d1b44e39afc8005271345ea5">doaj:7e6681b8d1b44e39afc8005271345ea5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hhqrydf2a5e3dg3zw4r7o5gdpq">fatcat:hhqrydf2a5e3dg3zw4r7o5gdpq</a> </span>
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