Open AccessLeveraging deep neural networks for anomaly‐based web application firewall
Author(s) -
Moradi Vartouni Ali,
Teshnehlab Mohammad,
Sedighian Kashi Saeed
Publication year - 2019
Publication title -
iet information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 34
eISSN - 1751-8717
pISSN - 1751-8709
DOI - 10.1049/iet-ifs.2018.5404
Subject(s) - computer science , autoencoder , artificial intelligence , artificial neural network , machine learning , deep learning , the internet , feature extraction , feature (linguistics) , data mining , firewall (physics) , support vector machine , world wide web , linguistics , philosophy , physics , schwarzschild radius , classical mechanics , gravitation , charged black hole
Web applications are the most common platforms for the exchange of information and services on the Internet. With the launch of web 2.0, information has flourished through social networking and business online. Therefore, websites are often attacked directly. As a result, the industry has paid more attention to the security of web applications in addition to security under computer networks. Intelligent systems based on machine learning have demonstrated excellent result on tasks such as anomaly detection in web requests. However, current methods based on traditional models cannot extract high‐level features from huge data. In this study, the authors proposed methods based on deep‐neural‐network and parallel‐feature‐fusion that features engineering as an integral part of them and plays the most important role in their performance. The proposed methods use stacked autoencoder and deep belief network as feature learning methods, in which only normal data is used in the classification of training phase, then, one‐class SVM, isolation forest, and elliptic envelope are applied as classifiers. The authors compared the proposed model with different strategies on CSIC 2010 and ECML/PKDD 2007 datasets. Results show that deep model and feature fusion model demonstrated as hierarchical feature learning which had better performance in terms of accuracy and generalisation in a reasonable time.