XKEYSCORE, ACLU document archive, slide #11.

Purpose:

XKEYSCORE is an NSA search and analysis system for data collected by other surveillance programmes. The system is described by Snowden as a search engine that provides a “one-stop shop” for access to content, metadata and real-time tracking and monitoring of user activities (COU01). Access to XKEYSCORE is shared with a number of other intelligence agencies including GCHQ (COU01, GUA01). In 2012, GCHQ’s TEMPORA programme was the largest source of XKEYSCORE data (EFF01).

The system incorporates user interfaces, databases and algorithms to select specific types of content and metadata that have already been collected by other surveillance programmes. Data can be retrieved using “strong selectors” such as email addresses and “soft selectors” such as keywords (ACU01). Rules for identifying particular kinds of data can be created and stored in the system. For example, analysts can target Tor users through rules that select web searches related to Tor and connections to the Tor network (NDR01). XKEYSCORE also has the ability to alert analysts to the activities of specific email and IP addresses (GUA02).

In 2008, the system included over 700 servers at approximately 150 locations around the world (ACU01). Content remains in the XKEYSCORE environment for three to five days, while metadata is stored for 30 days.

Capabilities (ACU01, EFF01):

• Ingestion of “full take” from NSA and partner agency bulk collection programmes.
• Federated query mechanism allows analysts to search multiple databases with a single query.
• Content and metadata can be searched using “strong selectors” and “soft selectors”.
• Rules for matching particular kinds of data can be created and stored in the system.
• Computer systems that are vulnerable to attack can be identified by monitoring network traffic.
• Documents can be traced back to their authors.
• Pattern-of-life analysis can develop profiles of individuals or find individuals matching a profile.

Data sources (ACU01, ELE01, SES01, WEE01):

• CIA/NSA Special Collection Service (F6).
• NSA Special Source Operations (such as PRISM, MUSCULAR and INCENSER).
• Foreign satellite data (FORNSAT).
• MARINA metadata repository.
• TRAFFICTHIEF metadata repository.

Related programmes (ACU01, EFF01, ELE01, SES01):

PRISM – NSA programme for content and metadata collection from service providers via the FBI.

MUSCULAR – GCHQ programme for bulk data collection from service provider data centres.

INCENSER – GCHQ programme for bulk data collection from fibre-optic cables.

TEMPORA – GCHQ programme for bulk data collection and buffering.

TRAFFICTHIEF – NSA repository for metadata about selected targets.

MARINA – NSA repository for bulk Internet metadata.

PINWALE – NSA repository for selected content.

Layers of operation:

• Network layer, transport layer and application layer: Matching content and metadata against rules defined by analysts.
• Social layer: Aggregation of content and metadata from multiple sources, pattern-of-life analysis.

Background:

XKEYSCORE training materials detail how analysts can use it and other systems to mine enormous agency databases by filling in a simple on-screen form giving only a broad justification for the search (GUA02). Requests are not reviewed by a court or any NSA personnel before being processed. The programme covers “nearly everything a typical user does on the internet”, including the content of emails, websites visited and searches, as well as their metadata (GUA02). The programme also allows for on-going “real-time” interception of an individual’s Internet activity (GUA02).

Data storage is an issue. According to leaked documents, “At some sites, the amount of data we receive per day (20+ terabytes) can only be stored for as little as 24 hours” (GUA02). In response, the NSA has created a multi-tiered system that allows analysts to store “interesting” content in other databases, such as one named PINWALE, which can store material for up to five years (GUA02).

Sources:

American Civil Liberties Union (ACU)
1) https://www.aclu.org/files/natsec/nsa/NSA%20XKeyscore%20Powerpoint.pdf

Courage Foundation (COU)
1) https://edwardsnowden.com/2014/01/27/video-ard-interview-with-edward-snowden

Electronic Frontier Foundation (EFF)
1) https://www.eff.org/files/2014/06/23/report_on_the_nsas_access_to_tempora.pdf

Electrospaces (ELE)
1) http://electrospaces.blogspot.co.uk/2014/11/incenser-or-how-nsa-and-gchq-are.html

Guardian (GUA)
1) http://www.theguardian.com/world/2013/jun/27/nsa-online-metadata-collection
2) http://www.theguardian.com/world/2013/jul/31/nsa-top-secret-program-online-data

NDR Panorama (NDR)
1) http://daserste.ndr.de/panorama/aktuell/NSA-targets-the-privacy-conscious,nsa230.html

Robert Sesek (SES)
1) https://robert.sesek.com/2014/9/unraveling_nsa_s_turbulence_programs.html

The Week (WEE)
1) http://theweek.com/articles/461482/4-nsa-terms-should-know

FASCIA, Washington Post, slide 1.

Purpose:

FASCIA is the US National Security Agency’s (NSA) data storage and analyse programme focused on mobile phone location metadata. Approximately 5 billion records per day are collected [WAH01]. The programme exploits the SS7 (Signaling System No. 7) data exchange protocol, which links mobile network providers together.

Two kinds of data are collected from mobile devices [WAH01].

• Information from phones, both mobile devices and landlines. This includes information held in these network such as location – known as Dialed Number Recognition (DNR) data.
• Information collected from the Internet – This includes personal data communications, known as Digital Network Intelligence (DNI).

Additionally it has the ability to analyse communication security (COMMSEC) behaviours such as Behaviours around communication security “frequent power-down, handset swapping, SMS behaviour” [NSA01].

The leaked documents show that the GCHQ works in partnership with the NSA in DNI collection, specifically to track location using the Google tracking cookie PREFID that is gathered with personal data communications. This cookie can be used to hack into devices [WAH02].

The FASCIA programme uses a variety of data analysis techniques to locate and track individuals using these two sources of data (DNR and DNI) including [WAH01; NSA01]:

• CHALKFUN: This is a ‘co-travel analytics’ tool that analyses “date, time, and network location of a mobile phone over a given time period, and then looks for other mobile phones that were seen in the same network locations around a one hour time window” [NSA01].
• DSD Co-Travel Analytic: Examines mobile Call Detail Records (CDRs) to predict “target locations and co-travelers by calculating time-based travel trajectories. Probable travel routes are calculated using observed locations and determining the most likely paths and travel times similar to that used in turn-by-turn navigation systems” [NSA01]. “The analytic predicts the approximate time that the target would theoretically arrive at each segment waypoint based on projected travel times between known locations.” It also “discovers candidate co-travellers that intersect locations along the buffered travel path.” The NSA whitepaper states that the “system has shown that more candidate co-travellers were discovered by analyzing the travel paths than by considering common meeting locations alone”. Future plans for the system include identifying “targets based on COMSEC behaviors such as identifying mobiles that are turned off right before convergence between two travel paths occurs”.
• TMI Co-Traveler Analytic: “The analytic is oriented to work on 7 to 30 days worth of regional collection.” It computes “target “closeness” based on latitude and longitude information.
• PACT NGA-NSA GATC Analytic: To identify Thuraya satellite phones.
• RT-RG Sidekicks: “compares average travel velocity between pairs of selectors to infer whether or not could co-travel would practically be possible. Locations are defined by CELL IDs (for GSM) or GEO-Hashes.”
• Scalable Analytics Tradecraft Center (SATC) Geospatial Lifelines Co-Travel QFD: This “applies the concept of “dwell times” to identify DNR co-travelers. Dwell times describe the time period spent at the beginning or ending destination. A location is considered a beginning or ending location if the dwell time at that location is greater than 2 hours.”
• SSG Common IMSIs Analytic: “Finds SIM card activity seen on cell tower panels in multiple areas (e.g.- border crossings commonly used by traffickers) … The analyst inputs areas of interest and time range. The analytic returns an excel file with a list of IMSIs seen in those areas at that time.”
• The Café project: “This analytic uses IP geolocation of active user/presence events as travel indication.” It focuses on targets who have travelled between two countries in a range of time between 30 days. It is also searchable by travel within “countries of interest” and “the days on which the countries were visited”.
• Other Data Sources: this includes information from other databases such as “air travelers on the same reservation number”, “users sharing a MAC address” and “similarities between IP addresses may indicate proximity on the same LAN” [NSA01].

Capabilities:

• Mobile phone network and internet analysis
• Pattern-of-life analysis

Data sources:

• Mobile networks
  • GCID: Global Cell-Tower ID – This is the unique number associated with any given tower. It acts as a proxy for location since
  • CELLID – mobile base station coordinates
  • VLR – (Visitor Location Registers); databases that track current associations between cellular users and towers, which can be used to infer a user’s location
  • IMSI – (International Mobile Subscriber Identity)
  • MSISDN – the telephone number associated with a SIM card indicating the country it was activated in and the service provider
• Internet data transfer
  • Mobile phone apps
  • IP address

Related programmes:

R6 SORTINGLEAD – Cloud-based version of CHALKFUN that includes additional features such as search by countries or locations of interest [NSA01].

HAPPY FOOT – Analytic tool that aggregates leaked location-based service data to map the physical locations of IP addresses [WAH01].

TAPERLAY –  The NSA’s tool for looking up the registered location of a mobile device — the provider and country where a phone was originally activated — in the Global Numbering Database [WAH01].

TUSKATTIRE – System used for metadata processing [WAH01]

JUGGERNAUT – A signals processing system that can process raw feeds between mobile carriers through the SS7 protocol [WAH01].

GHOSTMACHINE – The NSA’s cloud analytics platform [WAH03].

Layers of operation:

• Social layer: Aggregation of metadata from multiple sources, pattern-of-life analysis.
• Link layer – How devices connected to a physical layer share access to the physical medium and exchange data.
• Network layer – How data is routed between devices that may be connected to different link layers.
• Application layer – How applications provide services and exchange information over a transport layer.

Background:

FASCIA is the National Security Agency’s enormous database containing trillions of device-location records that are collected from a variety of sources. The leaked documents show the volume and types of device-location data collected. Mobile phone metadata analysis can reveal a high-level of detail regarding people’s movements.

When mobile devices are turned on and begin searching for wireless signals, they show their locations to any radio receivers in the vicinity. When a mobile phone connects to a network, it registers its location to one or more signalling towers that store this information in databases (known as Home Location Registers and Visitor Location Registers) maintained by telephone providers and clearing houses so that calls can be made and received.

These registers store a device’s approximate location using service providers positioning of devices by triangulating their distance between multiple towers in the vicinity. These can reveal the country, town, and even street level of the person. In addition, some mobile devices use WiFi and GPS signals to fix their locations, which provides geo-location data. Smartphones can also display their location through mobile apps, built-in location based services and IP addresses [WAH01].

Sources:

National Security Agency (NSA)   document, (provided by the Washington Post)

1) National Security Agency white paper: Summary of DNR and DNI Co-Travel Analytics
https://s3.amazonaws.com/s3.documentcloud.org/documents/888734/cotraveler-tracking-redacted.pdf

Washington Post (WAH)

1) http://www.washingtonpost.com/blogs/the-switch/wp/2013/12/10/new-documents-show-how-the-nsa-infers-relationships-based-on-mobile-location-data

2) http://apps.washingtonpost.com/g/page/world/nsa-signal-surveillance-success-stories/647

3) http://apps.washingtonpost.com/g/page/world/ghostmachine-the-nsas-cloud-analytics-platform/644/#document/p2/a135353

4) http://www.washingtonpost.com/world/national-security/nsa-tracking-cellphone-locations-worldwide-snowden-documents-show/2013/12/04/5492873a-5cf2-11e3-bc56-c6ca94801fac_story.html

The Intercept – Applying Advanced Cloud-based Behavior Analytics, slide 1.

Purpose:

SKYNET is a behaviour profiling programme that attempts to identify “interesting travel patterns”, including how often a person travels and to where [SKYNET-02, Slide13]. Specifically, the programme aims to identify “courier-like travel patterns” [SKYNET-02, Slide20].

It achieves this by analysing mobile phone metadata that reveals both location and communication data from bulk call records [INT01]. Using this metadata SKYNET looks for patterns amongst different people who use phones in similar ways [SKYNET-02, Slide2].

For this programme “call data is acquired from major Pakistani telecom providers” but the technical means for obtaining the data is not divulged in the slides [INT01]. It uses a cloud computing technology to store and analyse  Call Data Records (CDRs) from Pakistani Telecoms uploaded to an NSA cloud [SKYNET-01, Slide6]. Analysis of the data examines [SKYNET-02, Slide3]:

• Pattern of life
• Social network
• Travel behaviour

This is done using geospatial, geotemporal, pattern-of-life and travel analytics [SKYNET-01, Slide3]. Specifically, by identifying a mobile phone’s IMSI or International Mobile subscriber Identity [SKYNET-01, Slide13]. This number is a unique identification associated with all mobile phones on a cellular network. It is stored as a 64-bit field and is sent by the phone to the network [TFA01].

Behaviours SKYNET attempts to identify include [INT01]:

• Who has traveled from Peshawar to Faisalabad or Lahore (and back) in the past month?
• Who does the traveler call when he arrives?”
• “Excessive SIM or handset swapping,”
• “Incoming calls only,”
• “Visits to airports,”
• “Overnight trips”

Capabilities:

• Mobile phone metadata storage and analysis
• Pattern-of-life analysis
• Travel analysis
• Social network analysis

Data sources:

• Mobile phone metadata
• Global System for Mobile Communications (GSM)
• International Mobile Subscriber Identity (IMSI)

Related programmes:

DEMONSPIT – dataflow of Call Data Records (CDRs) from Pakistan [SKYNET-01, Slide6]

MAINWAY – collection of telephone metadata

Layers of operation:

• Social layer: Aggregation of metadata from multiple sources, pattern-of-life analysis.

Background:

The SKYNET programme collected 55 million cell phone records from Pakistan to identify ‘interesting’ or ‘suspect’ behaviours [INT01].

Questions are being raised about the “method of identifying terrorist targets based on metadata” [INT01] because it may identify false positives especially when it comes to the activities of journalists who seek to contact terrorists. In particular an Al Jazeera journalist, Ahmad Muaffaq Zaidan was singled out as someone whose “movements and calls mirrored those of known Al Qaeda couriers” [INT01].

Partners:

• MIT Lincoln Labs [SKYNET-01, slide 3]
• Harvard [SKYNET-01, slide 3]:
• Pakistan Telecoms (unnamed) [SKYNET-01, slide 6]

Sources:

Intercept (INT)
1) https://firstlook.org/theintercept/2015/05/08/u-s-government-designated-prominent-al-jazeera-journalist-al-qaeda-member-put-watch-list/
2) SKYNET01 – https://firstlook.org/theintercept/document/2015/05/08/skynet-applying-advanced-cloud-based-behavior-analytics/
3) SKYNET02 – https://firstlook.org/theintercept/document/2015/05/08/skynet-courier/

Tech Faq (TFA)
1) http://www.tech-faq.com/imsi.html

Purpose:

MARINA is an NSA repository for metadata. It stores information about millions of Internet users for up to a year (GUA01). The repository contains contact information, browsing history and other metadata. It also has the ability to export data in a variety of formats, including charts that assist in pattern-of-life analysis (GUA01).

MARINA aggregates metadata from a variety of sources, including online social networks, billing records, bank transactions, insurance information, passenger manifests, voter registration rolls, GPS location information, property records, and unspecified tax data (NYT01).

MAINWAY is the counterpart programme for storing telephone metadata (MOJ01).

Capabilities:

• Metadata storage and analysis
• Pattern-of-life analysis

Data sources:

• Internet traffic
• Commercial and financial transactions
• Travel records
• Government records

Related programmes:

XKEYSCORE – NSA system for searching and analysing data from a wide range of sources.

PRISM – NSA programme for collecting content and metadata from service providers via the FBI.

TEMPORA – GCHQ programme for bulk data collection and buffering.

MAINWAY – NSA repository for telephone metadata.

Layers of operation:

• Social layer: Aggregation of metadata from multiple sources, pattern-of-life analysis.

Background:

MARINA exploits a trend known as convergence, referred to in an NSA slide as “The gradual ‘blurring’ of telecommunications, computers, and the Internet” (ACU01).

This convergence of computerised data makes it easier to combine data from various sources, thus developing an understanding of both the social networks and the activities of people. MARINA is part of the Target Knowledge Database (TKB), a repository of data about targeted individuals including German Chancellor Angela Merkel (SPI01).

Sources:

American Civil Liberties Union (ACU)
1) https://www.aclu.org/sites/default/files/assets/social_convergence.pdf

Guardian (GUA)
1) http://www.theguardian.com/world/2013/sep/30/nsa-americans-metadata-year-documents

Mother Jones Magazine (MOJ)
1) http://www.motherjones.com/kevin-drum/2013/06/washington-post-provides-new-history-nsa-surveillance-programs

New York Times (NYT)
1) http://www.nytimes.com/2013/09/29/us/nsa-examines-social-networks-of-us-citizens.html

Spiegel (SPI)
1) http://www.spiegel.de/international/germany/gchq-and-nsa-targeted-private-german-companies-a-961444.html