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SFIM: Identify user behavior based on stable features
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DOI：10.1007/s12083-021-01214-2.png)
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Abstract
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The development of smartphones and social networks has brought great convenience to our lives. Due to the increasing requirements of user privacy, user data are protected by encryption protocol. Nevertheless, the encrypted traffic may still be identificated by a third party. In order to improve the privacy protection of users, it is necessary to study the existing encrypted user behavior system. The existing user behavior identification adopts the statistical features of encrypted traffic, which fluctuates greatly in different transmission environments. In this paper, we propose a Stable Features Identification Method(SFIM), which concentrate on filtering out the stable features from the encrypted traffic to identify user behavior. Based on the principle of maximum entropy, we put forward an approach to divide the distribution ranges of these stable features, and map the feature space into vector space. Our research focuses on multiple user behavior in the Instagram application. The best evaluation results achieve 99.8% accuracy, 99.3% precision, 99.3% recall, and 0.09% false positive rate(FPR) on average.
Keywords:
Social network
User behavior
Stable features
Feature space
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