A Hybrid Method of Genetic Algorithm and Support Vector Machine for DNS Tunneling Detection

fuqdan al-ibraheemi

Abstract


With the expansion of the business over the internet, corporations nowadays are investing numerous amounts of money in the web applications not only for making profit but also for storing their information, communicating with their clients and accommodating transactions. However, there are different threats could make the corporations vulnerable for potential attacks. One of the significant threats is the DNS tunneling which is an attack that exploit the domain name protocol in order to bypass security gateways. This would lead to lose critical information which is a disastrous situation for many organizations. Recently, researchers have paid more attention in the machine learning techniques regarding the process of DNS tunneling. In their approaches, the authors have used different and numerous types of features such as domain length, number of bytes, content, volume of DNS traffic, number of hostnames per domain, geographic location and domain history. Apparently, there is a vital demand to accommodate feature selection task in order to identify the best features. This paper aims to propose a hybrid method of Genetic Algorithm feature selection approach with the Support Vector Machine classifier for the sake of identifying the best features that have the ability to optimize the detection of DNS tunneling. To evaluate the proposed method, a benchmark dataset of DNS tunneling has been used. Results showed that the proposed method has outperformed the conventional SVM by achieving 0.946 of f-measure.

Keywords


DNS Tunneling; Support Vector Machine; Genetic Algorithm; Feature Selection

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DOI: http://doi.org/10.11591/ijece.v11i1.pp%25p
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ISSN 2088-8708, e-ISSN 2722-2578