A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Gormez Y., AYDIN Z., Karademir R., GÜNGÖR V. Ç.

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, cilt.33, sa.11, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33 Sayı: 11
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1002/dac.4401
  • Dergi Adı: INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high-profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder-based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi-class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.