Active User-Side Evil Twin Access Point Detection Using Statistical Techniques

摘要

In this paper, we consider the problem of “evil twin” attacks in wireless local area networks (WLANs). An evil twin is essentially a rogue (phishing) Wi-Fi access point (AP) that looks like a legitimate one (with the same SSID). It is set up by an adversary, who can eavesdrop on wireless communications of users'' Internet access. Existing evil twin detection solutions are mostly for wireless network administrators to verify whether a given AP is in an authorized list or not, instead of for a wireless client to detect whether a given AP is authentic or evil. Such administrator-side solutions are limited, expensive, and not available for many scenarios. Thus, a lightweight, effective, and user-side solution is highly desired. In this work, we propose a novel user-side evil twin detection technique that outperforms traditional administrator-side detection methods in several aspects. Unlike previous approaches, our technique does not need a known authorized AP/host list, thus it is suitable for users to identify and avoid evil twins. Our technique does not strictly rely on training data of target wireless networks, nor depend on the types of wireless networks. We propose to exploit fundamental communication structures and properties of such evil twin attacks in wireless networks and to design new active, statistical and anomaly detection algorithms. Our preliminary evaluation in real-world widely deployed 802.11b and 802.11 g wireless networks shows very promising results. We can identify evil twins with a very high detection rate while maintaining a very low false positive rate.