SeaGlass: Enabling City-Wide IMSI-Catcher Detection

Abstract

Cell-site simulators, also known as IMSI-catchers and stingrays, are used around the world by governments and criminals to track and eavesdrop on cell phones. Despite extensive public debate surrounding their use, few hard facts about them are available. For example, the richest sources of information on U.S. government cell-site simulator usage are from anonymous leaks, public records requests, and court proceedings. This lack of concrete information and the difficulty of independently obtaining such information hampers the public discussion. To address this deficiency, we build, deploy, and evaluate SeaGlass, a city-wide cellsite simulator detection network. SeaGlass consists of sensors that measure and upload data on the cellular environment to find the signatures of portable cell-site simulators. SeaGlass sensors are designed to be robust, low-maintenance, and deployable in vehicles for long durations. The data they generate is used to learn a city’s network properties to find anomalies consistent with cell-site simulators. We installed SeaGlass sensors into 15 ridesharing vehicles across two cities, collecting two months of data in each city. Using this data, we evaluate the system and show how SeaGlass can be used to detect signatures of portable cell-site simulators. Finally, we evaluate our signature detection methods and discuss anomalies discovered in the data.

Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps‐based analysis of Wuhan, China

“…At the same time, we used the land cover data and OSM map data to extract the geographic elements of impervious surfaces, water bodies, and green spaces in Wuhan, then calculated the ratio grid of each element. Depending on the size of our study area, the grid accuracy can range from 10 to 1,000 m. Referring to the previous urban grid‐based analysis (DiCenzo & Wertheim, ; Kalashnikov, Prabhakar, Hambrusch, & Aref, ; Liu et al, ; Ney, Smith, Cadamuro, & Kohno, ; Yan, Li, & Lin, ), we select a moderate gird size of 100 × 100 m for data collection.…”

On Security Research Towards Future Mobile Network Generations

“…2) Defenses: Similar to unsecured pre-authentication traffic, fake base stations that exploit the non-existing mutual authentication can be detected with mobile apps, baseband firewalls, sensors, or network-based sensors [64], [65], [66], [67], [68], [69], [110]. Nevertheless, mobile apps do not sufficiently protect against the threat of fake base stations [111].…”

The Signpost Platform for City-Scale Sensing

“…Energy Network Processing Storage Time Sync Location Caraoke [1] Bouillet et al [10] AirCloud [14] Girod et al [22] Lédeczi et al [27] SenseFlow [31] Argos [50] SONYC [41] Kyun Queue [51] Micronet [24] Seaglass [43] Table 1: Services required by existing applications. Time is millisecond-accurate as provided by services like NTP, while Sync is microsecond-accurate as provided by GPS.…”

“…Many have paired mobile phones with handheld air quality monitors [7,14,17,18], or used phones to directly meter noise pollution [9,35,47] or traffic conditions [38,54,61]. Similar to participatory sensing methods, vehicular sensor networks monitor air quality, traffic, and road conditions [17,19,23,29,36], and even detect rogue cellular base stations [43]. These types of deployments often scale very well as the mobility of the devices allows a few sensors to reach a much larger area.…”