DESIGN AND IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE-BASED WEB APPLICATION FIREWALL MODEL

Abstract

Attacks on web applications and web-based services were conducted using Hyper-Text Transfer Protocol (HTTP), which is also used as the communication protocol of web-based applications.Due to the dynamic structure of web applications and the fact that they have many variables, detection and prevention of web-based attacks are made more difficult.In this study, a hybrid learning-based web application firewall (WAF) model is proposed to prevent web-based attacks, by using signature-based detection (SBD) and anomaly-based detection (ABD).Detection of known web-based attacks is done by using SBD, while detection of anomaly HTTP requests is done by using ABD.Learning-based ABD is implemented by using Artificial Neural Networks (ANN).Thus, an adaptation of the model against zero-day attacks is ensured by learning-based ABD by using ANN.The proposed model is tested by using WAF 2015, CSIC 2010 and ECML-PKDD datasets which are open source datasets.According to the test results, a high mean achievement percentage (96.59%) was obtained.Detection results are also compared to previous studies.After comparison, the proposed model promises higher performance than what the existing studies until now have to offer.

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