| Title | An Overview of Machine Learning Based Approaches in DDoS Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Atasever, Süreyya, Öz\c celık, İlker, Sa\u giro\u glu, \c Seref |
| Conference Name | 2020 28th Signal Processing and Communications Applications Conference (SIU) |
| Date Published | Oct. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-7206-4 |
| Keywords | composability, Computer crime, DDoS attack detection, DDoS detection, denial-of-service attack, distributed denial of service, feature extraction, Human Behavior, machine learning, Metrics, pubcrawl, Reactive power, resilience, Resiliency, supervised learning, Support vector machines |
| Abstract | Many detection approaches have been proposed to address growing threat of Distributed Denial of Service (DDoS) attacks on the Internet. The attack detection is the initial step in most of the mitigation systems. This study examined the methods used to detect DDoS attacks with the focus on learning based approaches. These approaches were compared based on their efficiency, operating load and scalability. Finally, it is discussed in details. |
| URL | https://ieeexplore.ieee.org/document/9302121 |
| DOI | 10.1109/SIU49456.2020.9302121 |
| Citation Key | atasever_overview_2020 |
An Overview of Machine Learning Based Approaches in DDoS Detection