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A Passive Client-based Approach to Detect Evil Twin Attacks

机译:一种基于客户的基于客户的方法来检测邪恶的双胞胎攻击

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As the widespread deployment and usage of 802.11-based wireless local area networks (WLANs), Wi-Fi users are vulnerable to be attacked by a security threat called evil twins. The evil twin, a kind of rogue access points (RAPs), masquerades as a legitimate access point (AP) to lure users to connect it. Malicious adversaries can easily configure evil twins on a laptop to induce victim wireless users. The presence of such a threat continuously leads to significant loss of information. In this paper, we propose a passive client-side detection approach that allows users to independently identify and locate evil twins without any assistance from a wireless network administrator. Because of the forwarding behavior of evil twins, proposed method compares 802.11 data frames sent by target APs to users to determine evil twin attacks. We implemented our detection and location technique in a Python tool named ET-spotter. Through implementation and evaluation in our study, our algorithm achieves 96% accuracy in distinguishing evil twins from legitimate APs.
机译:作为基于802.11的无线局域网(WLAN)的广泛部署和使用,Wi-Fi用户容易被称为邪恶双胞胎的安全威胁攻击。邪恶的双胞胎,一种流氓接入点(Raps),伪装为诱导用户连接它的合法接入点(AP)。恶意的对手可以轻松在笔记本电脑上配置邪恶的双胞胎,以诱导受害者无线用户。这种威胁的存在不断导致大量信息损失。在本文中,我们提出了一种被动的客户端检测方法,允许用户在没有无线网络管理员的任何帮助下独立识别和定位邪恶的双胞胎。由于邪恶双胞胎的转发行为,所提出的方法比较了目标APS向用户发送的802.11数据帧来确定邪恶的双击攻击。我们在名为Et-Spotter的Python工具中实现了我们的检测和位置技术。通过我们研究的实施和评估,我们的算法在与合法的APS中区分邪恶双胞胎的准确度得到了96%的准确性。

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