Scalable, Semi supervised Mobile App Fingerprinting using LSH

Application fingerprinting is essential for network management and security, enabling accurate traffic classification and the enforcement of Quality of Service (QoS) policies. In this work, we propose a scalable method for mobile application fingerprinting that leverages MinHash and Locality-Sensitive Hashing (LSH) to efficiently identify behavioral similarities in encrypted network traces. By restricting comparisons to highsimilarity candidates, our approach significantly reduces computational complexity while preserving accuracy and enabling the detection of previously unseen applications. Evaluated on the ReCon dataset, the method achieves an average accuracy of 83% across app identification and unseen app detection tasks, with a reduction in comparison complexity from O(n2) to O(n log n).

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