- Title
- A server side solution for detecting webInject : A machine learning approach
- Creator
- Moniruzzaman, Md; Bagirov, Adil; Gondal, Iqbal; Brown, Simon
- Date
- 2018
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/167940
- Identifier
- vital:13792
- Identifier
-
https://doi.org/10.1007/978-3-030-04503-6_16
- Identifier
- ISBN:03029743 (ISSN); 9783030045029 (ISBN)
- Abstract
- With the advancement of client-side on the fly web content generation techniques, it becomes easier for attackers to modify the content of a website dynamically and gain access to valuable information. A majority portion of online attacks is now done by WebInject. The end users are not always skilled enough to differentiate between injected content and actual contents of a webpage. Some of the existing solutions are designed for client side and all the users have to install it in their system, which is a challenging task. In addition, various platforms and tools are used by individuals, so different solutions needed to be designed. Existing server side solution often focuses on sanitizing and filtering the inputs. It will fail to detect obfuscated and hidden scripts. In this paper, we propose a server side solution using a machine learning approach to detect WebInject in banking websites. Unlike other techniques, our method collects features of a Document Object Model (DOM) and classifies it with the help of a pre-trained model.
- Publisher
- Springer Verlag
- Relation
- 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018; Melbourne, Australia; 3rd June 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11154 LNAI, p. 162-167
- Rights
- Copyright © Springer Nature Switzerland AG 2018.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Machine learning; Server side detection; WebInject
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