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A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks
Author(s) -
Gormez Yasin,
Aydin Zafer,
Karademir Ramazan,
Gungor Vehbi C.
Publication year - 2020
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4401
Subject(s) - autoencoder , computer science , artificial intelligence , machine learning , denial of service attack , deep learning , ensemble learning , bayesian optimization , binary classification , the internet , support vector machine , world wide web
Summary 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.