Automatic Detection of HTTP Injection Attacks using Convolutional Neural Network and Deep Neural Network

Keywords: HTTP, injection attack, DNN, CNN, cyber security, deep learning

Abstract

HTTP injection attacks are well known cyber security threats with fatal consequences. These attacks initiated by malicious entities (either human or computer) send dangerous or unsafe malicious contents into the parameters of HTTP requests. Combatting injection attacks demands for the development of Web Intrusion Detection Systems (WIDS). Common WIDS follow a rule-based approach or a signature-based approach which have the common problem of high false-positive rate (wrongly classifying malicious HTTP requests) hence making them restricted to only one type of web application. They are easily bypassed and unable to detect new kinds of malicious attacks as they lack a sufficient model of understanding the representations of HTTP request parameters. In this paper, deep learning techniques are used to develop models that would automatically detect injection attacks in HTTP requests. A special layer called the character embedding layer in the deep learning models is used to allow the learning of the representation of the request parameter of HTTP requests in higher abstract levels and also aid in learning the relationships between the characters of the request parameter. The experimentation results showed that with deep learning, better injection attack detection is possible and given the right dataset, a deep learning detection model would be able to correctly classify HTTP requests for any web application.

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Author Biographies

Victor Odumuyiwa, Department of Computer Science, University of Lagos, Nigeria

Victor Odumuyiwa obtained his PhD in 2010 from Nancy 2 University in France. He was an ETT fellow at the Massachusetts Institute of Technology, Boston, USA in 2013. He is a certified security intelligence analyst and application security analyst. He is also a member of the Global EPIC eco-system centred on innovation and cyber security. He is the Director of the Centre for Information Technology and Systems (CITS) and a Senior Lecturer at the Department of Computer Science, University of Lagos, Nigeria.

Analogbei Chibueze, Department of Computer Sciences, University of Lagos, Nigeria

Analogbei Chibueze is a graduate of computer science from the University of Lagos, Nigeria with the passion for research and development of critical problem solving software solutions. He’s research areas include artificial intelligence, deep learning, and modelling and simulation.

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Published
2021-02-09
Section
Articles