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Moving Target Defense for Web Applications

abstract: Web applications continue to remain as the most popular method of interaction for businesses over the Internet. With it's simplicity of use and management, they often function as the "front door" for many companies. As such, they are a critical component of the security ecosystem as vulnerabilities present in these systems could potentially allow malicious users access to sensitive business and personal data.

The inherent nature of web applications enables anyone to access them anytime and anywhere, this includes any malicious actors looking to exploit vulnerabilities present in the web application. In addition, the static configurations of these web applications enables attackers the opportunity to perform reconnaissance at their leisure, increasing their success rate by allowing them time to discover information on the system. On the other hand, defenders are often at a disadvantage as they do not have the same temporal opportunity that attackers possess in order to perform counter-reconnaissance. Lastly, the unchanging nature of web applications results in undiscovered vulnerabilities to remain open for exploitation, requiring developers to adopt a reactive approach that is often delayed or to anticipate and prepare for all possible attacks which is often cost-prohibitive.

Moving Target Defense (MTD) seeks to remove the attackers' advantage by reducing the information asymmetry between the attacker and defender. This research explores the concept of MTD and the various methods of applying MTD to secure Web Applications. In particular, MTD concepts are applied to web applications by implementing an automated application diversifier that aims to mitigate specific classes of web application vulnerabilities and exploits. Evaluation is done using two open source web applications to determine the effectiveness of the MTD implementation. Though developed for the chosen applications, the automation process can be customized to fit a variety of applications. / Dissertation/Thesis / Masters Thesis Computer Science 2018

Identiferoai:union.ndltd.org:asu.edu/item:49042
Date January 2018
ContributorsTaguinod, Marthony (Author), Ahn, Gail-Joon (Advisor), Doupé, Adam (Advisor), Yau, Sik-Sang (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeMasters Thesis
Format66 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

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