Mobile App Fingerprinting through Automata Learning and Machine Learning

Abstract

Application fingerprinting is crucial in network management and security to provide the best Quality of Service (QoS). To generate fingerprints for applications, we use an automata learning algorithm to observe the temporal order among destination-related features of network traffic and create a language as a fingerprint. We label fingerprints through machine learning classifiers. We propose our approach in a framework called ML-NetLang for fingerprinting mobile applications from encrypted network traffic. Our evaluation achieves an average accuracy of 95% for Android and iOS applications. ML-NetLang outperforms comparable state-of-the-art techniques using behavioral-based, correlation-based, and machine-learning solutions.