Smart Spying via Deep Learning: Inferring Your Activities from Encrypted Wireless Traffic
Smart Spying via Deep Learning: Inferring Your Activities from Encrypted Wireless Traffic
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DOI：
10.1109/globalsip45357.2019.8969428
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Tao Hou;Tao Wang;Zhuo Lu;Yao Liu
中科院分区：
文献类型：
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作者：
Tao Hou;Tao Wang;Zhuo Lu;Yao Liu
无线网络在我们的日常生活中无处不在。由于开放信道的特性，无线网络很容易受到窃听攻击。虽然无线对话可以被加密以防止窃听，但攻击者仍然可以通过对加密数据的流量分析来推断用户的活动。然而，以往的推理方法通常具有局限性，它们只能在特定的领域内达到相对较高的精度。在本文中，我们提出了一个智能间谍策略，可以推断用户的活动，多个域具有较高的准确性。我们还开发了一个原型工具，在此策略之上进行实验。实验结果表明，该策略能够有效地对加密数据进行活动推断，准确率高达99.17%。
Wireless networks nowadays are ubiquitous in our daily life. Due to the open channel nature, wireless networks are vulnerable to eavesdropping attacks. Though wireless conversation can be encrypted against eavesdropping, attackers can still infer a user’s activities via traffic analysis on encrypted data. Nevertheless, previous inference methods usually have the limitation that they can only achieve a relatively high accuracy in a specific domain. In this paper, we propose a smart spying strategy that can infer a user’s activities of multiple domains with a higher accuracy. We also develop a prototype tool on top of this strategy to conduct experiments. The evaluation results show our strategy works effectively in activity inference on encrypted data, with an accuracy rate as high as 99.17%.