Digital Terrestrial Tracking: The Future of Surveillance

In this paper, the terms Digital Terrestrial Tracking (DTT) and Digital Terrestrial Footprint (DTF) are introduced. The DTF defines the uniquely identifiable signature of wireless signals emitted by a device or collection of devices that an individual carries on their person in the physical world. These signals can reveal a device’s history at a location and point in time, and potentially disclose details about the owner. Interrogation or interaction with the device may reveal further details. The DTF positions itself between an individual’s physical world footprint (their unique personal attributes), and their online footprint (defined by their unique online persona). Physical world tracking would involve following a person based on what they look or sound like; online tracking would involve tracking a person online activity based on their unique online signature (cookies, IP addresses, social media accounts); and digital terrestrial tracking involves tracking a person in the real world based on a unique signature emitted by devices on their person. The goal of the research conducted and discussed in this paper was to build a mass data collection and correlation framework based on information leaked from the wireless devices that people carry. The framework should be able to identify, track, and profile people by passively collected wireless information from devices, and collect information that is more verbose by optionally interrogating devices. The result is a tool, named Snoopy, written in Python, capable of operating in a distributed manner, in harsh environments on affordable off the shelf (OTS) hardware. Snoopy is able to draw specific and high level conclusions about individuals based on their digital wireless signals. The framework has been extensively tested in busy public areas (such as conferences, airports, hotels, etc.) and validated our hypothesis that such tracking was possible. Analysis performed against the collected data revealed interesting insights and trends, which will be discussed in the results section of this paper.

[1]  Mathieu Cunche,et al.  I know your MAC address: targeted tracking of individual using Wi-Fi , 2014, Journal of Computer Virology and Hacking Techniques.

[2]  Roksana Boreli,et al.  Inferring user relationship from hidden information in WLANs , 2012, MILCOM 2012 - 2012 IEEE Military Communications Conference.

[3]  Jiefeng Chen,et al.  Why are they hiding? Study of an anonymous file sharing system , 2012, 2012 IEEE First AESS European Conference on Satellite Telecommunications (ESTEL).

[4]  Alessandro Epasto,et al.  Signals from the crowd: uncovering social relationships through smartphone probes , 2013, Internet Measurement Conference.

[5]  A. B. M. Musa,et al.  Tracking unmodified smartphones using wi-fi monitors , 2012, SenSys '12.

[6]  Gerhard P. Hancke,et al.  Eavesdropping Attacks on High-Frequency RFID Tokens , 2008 .

[7]  Roksana Boreli,et al.  I know who you will meet this evening! Linking wireless devices using Wi-Fi probe requests , 2012, 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[8]  Ashkan Soltani,et al.  Tiny Constables and the Cost of Surveillance: Making Cents Out of United States v. Jones , 2007 .

[9]  Roksana Boreli,et al.  Linking wireless devices using information contained in Wi-Fi probe requests , 2014, Pervasive Mob. Comput..

[10]  Hannes Federrath,et al.  Protection in Mobile Communications , 1999 .

[11]  Mike Ryan,et al.  Bluetooth: With Low Energy Comes Low Security , 2013, WOOT.

[12]  Ravishankar Borgaonkar,et al.  Femtocells: a Poisonous Needle in the Operator's Hay Stack , 2011 .