Passive cyber-security attacks do not require any modification of the data stream generated by the victim, nor the creation of a false statement; in particular, those attacks based on statistical analysis aim at acquiring sensible information by just analyzing traffic patterns. Our work sits on the conjecture that the PDCCH, which is transmitted in clear text, may be effectively used to statistically characterize the traffic generated by a smartphone in standby mode. Through this statistical signature, the attacker may then infer whether an unknown traffic pattern is generated by the victim user's terminal, guessing if the victim is in a certain geographical area, and in turn gaining the ability to track the victim's movements and/or to profile their habits. In this work, we propose a data collection and processing framework that successfully obtains such signatures. User data patterns (transport block sizes and communications direction) are retrieved by analyzing the mobile network scheduling. Hence, a sequence-to-sequence learning framework to extract smartphone signatures from passive traffic is put forward, and is experimentally validated using a dataset of 40 user traces, successfully identifying up to 90 percent of the users.

中文翻译：

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通过被动交通指纹识别智能手机：一种序列到序列的学习方法


被动网络安全攻击不需要对受害者生成的数据流进行任何修改，也不需要创建虚假陈述；特别是，那些基于统计分析的攻击旨在通过分析流量模式来获取敏感信息。我们的工作基于这样的猜想：以明文传输的 PDCCH 可以有效地用于统计表征智能手机在待机模式下生成的流量。通过此统计签名，攻击者可以推断受害者用户的终端是否生成了未知的流量模式，猜测受害者是否位于某个地理区域，进而获得跟踪受害者的移动和/或分析的能力他们的习惯。在这项工作中，我们提出了一个成功获得此类签名的数据收集和处理框架。通过分析移动网络调度来检索用户数据模式（传输块大小和通信方向）。因此，提出了一种从被动流量中提取智能手机签名的序列到序列学习框架，并使用 40 个用户痕迹的数据集进行了实验验证，成功识别了高达 90% 的用户。