DDoS Attacks Detection by Using Machine Learning Methods on Online Systems

Baskaya D., Samet R.

5th International Conference on Computer Science and Engineering (UBMK), Diyarbakır, Türkiye, 9 - 11 Eylül 2020, ss.52-57 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/ubmk50275.2020.9219476
  • Basıldığı Şehir: Diyarbakır
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.52-57
  • Anahtar Kelimeler: Cyber Security, Machine Learning, Cyber Attacks, Distributed Denial of Service Attacks, DDoS
  • Ankara Üniversitesi Adresli: Evet

Özet

DDoS attacks impose serious threats to many large or small organizations; therefore DDoS attacks have to be detected as soon as possible. In this study, a methodology to detect DDoS attacks is proposed and implemented on online systems. In the scope of the proposed methodology, Multi Layer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN). C-Support Vector Machine (SVC) machine learning methods are used with scaling and feature reduction preprocessing methods and then effects of preprocesses on detection accuracy rates of HTTP (Hypertext Transfer Protocol) flood, TCP SYN (Transport Control Protocol Synchronize) flood, UDP (User Datagram Protocol) flood and ICMP (Internet Control Message Protocol) flood DDoS attacks are analyzed. Obtained results showed that DDoS attacks can be detected with high accuracy of 99.2%.