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Real-time Identification of Rogue WiFi Connections Using Environment-Independent Physical Features

机译:使用与环境无关的物理特征实时识别恶意WiFi连接

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WiFi has become a pervasive communication medium in connecting various devices of WLAN and IoT. However, WiFi connections are vulnerable to the impersonation attack from rogue access points (AP) or devices, whose SSID and/or MAC/IP address are identical to the legitimate devices. This kind of attack is difficult to countermeasure with traditional network security mechanisms. In this paper, we present a novel security mechanism to detect and identify rogue WiFi devices or AP using environment-independent characteristics extracted from channel state information (CSI), and refuse their connections. We find that nonlinear phase errors of different subcarriers change with WiFi network interface cards (NIC), due to the I/Q imbalance and imperfect oscillator of each WiFi NIC. Validated by our experiments, this phase feature across subcarriers is consistent and invariant to location and external environment, and can be extracted to build an essential signature of the NIC itself. Such signature of the transmitter can be calculated in real-time by the receiver and cannot be forged by rogue devices. Extensive experiments with dozens of WiFi devices demonstrate that the proposed mechanism can reliably detect the rogue WiFi connections and prevent impersonation in various scenarios. The speed of identification is 8× faster than that of the state-of-the-art solution. Moreover, the accuracy of rogue connection detection is up to 96% and false alarm rate is shown below 2%.
机译:WiFi已成为连接WLAN和IoT的各种设备的普遍通信介质。但是,WiFi连接容易受到来自恶意访问点(AP)或SSID和/或MAC / IP地址与合法设备相同的设备的模拟攻击。使用传统的网络安全机制很难对付这种攻击。在本文中,我们提出了一种新颖的安全机制,可以使用从信道状态信息(CSI)中提取的与环境无关的特征来检测和识别恶意WiFi设备或AP,并拒绝它们的连接。我们发现,由于每个WiFi NIC的I / Q不平衡和不完美的振荡器,不同子载波的非线性相位误差随WiFi网络接口卡(NIC)的变化而变化。通过我们的实验验证,跨子载波的​​相位特征对于位置和外部环境是一致且不变的,可以提取出来以构建NIC本身的基本特征。发送器的这种签名可以由接收器实时计算,并且不能被流氓设备伪造。数十种WiFi设备的大量实验表明,该机制可以可靠地检测恶意WiFi连接并在各种情况下防止假冒。识别速度比最新解决方案快8倍。此外,流氓连接检测的准确性高达96%,错误警报率显示为低于2%。

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