Vulnerability Detection on Mobile Applications Using State Machine Inference

Vulnerability Detection on Mobile Applications Using State Machine Inference

van der Lee, Wesley (Student TU Delft)
Verwer, S.E. (TU Delft Cyber Security)

2018

Although the importance of mobile applications grows every day, recent vulnerability reports argue the application's deficiency to meet modern security standards. Testing strategies alleviate the problem by identifying security violations in software implementations. This paper proposes a novel testing methodology that applies state machine learning of mobile Android applications in combination with algorithms that discover attack paths in the learned state machine. The presence of an attack path evidences the existence of a vulnerability in the mobile application. We apply our methods to real-life apps and show that the novel methodology is capable of identifying vulnerabilities.

mobile application security
model inference
State machine learning
vulnerability detection

http://resolver.tudelft.nl/uuid:1d8e42cd-9823-4f08-8e75-55db33019d69

https://doi.org/10.1109/EuroSPW.2018.00008

IEEE, Los Alamitos, CA

978-1-5386-5445-3

Proceedings - 3rd IEEE European Symposium on Security and Privacy Workshops, EUROS&PW 2018

3rd IEEE European Symposium on Security and Privacy Workshops, 2018-04-24 → 2018-04-26, London, United Kingdom

Accepted author manuscript

Institutional Repository

conference paper

© 2018 Wesley van der Lee, S.E. Verwer