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'Dishfire' program - NSA collects millions of sms daily

'Dishfire' program - NSA collects millions of sms daily



'Dishfire' program - NSA collects millions of text messages daily in 'untargeted' global sweep: ...

'Dishfire' program - NSA collects millions of text messages daily in 'untargeted' global sweep:
• NSA extracts location, contacts and financial transactions
• 'Dishfire' program sweeps up 'pretty much everything it can'
• GCHQ using database to search metadata from UK numbers

The National Security Agency has collected almost 200 million text messages a day from across the globe, using them to extract data including location, contact networks and credit card details, according to top-secret documents.

The untargeted collection and storage of SMS messages – including their contacts – is revealed in a joint investigation between the Guardian and the UK’s Channel 4 News based on material provided by NSA whistleblower Edward Snowden.

The documents also reveal the UK spy agency GCHQ has made use of the NSA database to search the metadata of “untargeted and unwarranted” communications belonging to people in the UK.

The NSA program, codenamed Dishfire, collects “pretty much everything it can”, according to GCHQ documents, rather than merely storing the communications of existing surveillance targets.

The NSA has made extensive use of its vast text message database to extract information on people’s travel plans, contact books, financial transactions and more – including of individuals under no suspicion of illegal activity.

An agency presentation from 2011 – subtitled “SMS Text Messages: A Goldmine to Exploit” – reveals the program collected an average of 194 million text messages a day in April of that year. In addition to storing the messages themselves, a further program known as “Prefer” conducted automated analysis on the untargeted communications.

The Guardian



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    'Dishfire' program - NSA collects millions of sms daily 'Dishfire' program - NSA collects millions of sms daily Presentation Transcript

    • TOP SECRET//COMJNT//REL TO USA, FVEY//20320108 Content Extraction Enhancements For Target Analytics: SMS Text Messages: A Gold mine to Exploit 9 June, 2011 Presenters: Work funded by T1221 Center for Content Extraction Performed in Collaboration with 1221 Center for Content Extraction T132 Dishfi re ..----De-n- F-ro_m_ ·ve-d : N-SAJ-C-SS-M-1--.,52 Dated: 20070108 _ Declassify On: 20341201 TOP SECRET//COMJNT//REL TO USA, FVEY//20320108
    • SECRET//COMINT//REL TO USA, FVEY//20320108 (U) OUTLINE • (U) Introduction & Some Statistics • (U) Missed Call Messages • (U) MilkBone QFD "Demo" • (U) Where Next? SECRET//COMINT//REL TO USA, FVEY//20320108 2
    • UNCLASSIFIED (U)SMS (Short Message Service) some stats • (U) (May 2011 ): Mobile phone subscriptions have reached 5.3 billion, 77% of the world population. Growth led by China and India . • (U) 500 million people accessed mobile internet worldwide in 2009. Usage is expected to double in 5 years. 1/2011 : 200 million users access Facebook using mobile. • (U)(Oct. 201 0) Many mobile Web users are mobile-only (rarely use desktop, laptop or tablet to access the Webl). Mobile-only in Egypt is 70%, India 59% and US 25% . Mobile penetration in the developing world is now at 68%, • (U) SMS is still king of mobile messaging- 6.1 trillion messages sent in 2010 (200,000 text messages per second) and is expected to exceed 10 trillion in 2013 (1.8 trillion sent in 2007). Most number of texts are sent in the Philippines and US. • (U) Mobile phone providers in developing countries increasingly use the mobile phone for health services and banking (International Telecommunications Union) • (U) Many mobile web users do not have a bank account (India 57%). Gartner predicts that the number 1 service in 2010 will be money transfer using SMS. Estimate 2009 55 million users and various organizations predict doubling every year estimate 2013 around 5 million user). Initiatives to bank the unbanked. • (U) The typical mobile subscriber sends and receives more SMS text messages than telephone calls. The average U.S. mobile customer sent or received 357 text messages in 2008 (a 450% increase over 2006) and placed/received 204 calls. In 2010, the average American teen sent or received 3,339 texts per month, > 6 per hour. • (U) 2008 estimate of text message usage among wireless subscribers: Russia- 88%, UK- 76%, China- 72%, Brazil- 60%, USA- 53% UNCLASSIFIED
    • SECRET//COMINT//REL TO USA, FVEY//20320108 ~-::F~____.. U) SMS Message Componen • (U) SMS Metadata - (U) IMSI: International Mobile Subscriber Identity (most frequent) - (U) MSISDN: Mobile Subscriber Integrated Services Digital Network Number, i.e., phone number - (U) IMEI: International Mobile Equipment Identity - (U) SME: Short Message Entity (entities which can send & receive messages) • Content - (U) Typed Text Message • (U) User entered • (U) System Generated - (U) Useful (personal) [Ham] - (U) Spam SECRET//COMINT//REL TO USA, FVEY//20320108
    • SECRET//COMINT//REL TO USA, FVEY//20320108 (U) Why? MSISDN (phone#) IMSI (person id) IMEI (equipment) METACONTENT: Message Content • (S//REL) Metadata + Content of System Generated Text Messages leads to analytic gems => content derived meta data • (S//SI//REL) Such gems often are not in current metadata stores and would enhance current analytics: contact chaining, geolocation, alternative identifiers (including DNI & DNR links), travel, finance • (S//REL) SMS: Rich data set, high impact. Usage is increasing. Features & Notifications available on mobile phones are increasing ~ rich data set awaiting exploitation . SECRET//COMINT//REL TO USA, FVEY//20320108 5
    • SECRET//COMINT//REL TO USA, FVEY//20320108 (U) PREFER • (U//FOUO) Identifies types of automated messages • (U//FOUO) Extracts entities from SMS content daily: • (S//REL) Results presented averaged over 30 days (April 2011) - 194,184,810- sms messages per day (not deduped) - 184,794,279 - DISHFIRE message tags - 188,299,963 - PREFER text slice decoded • (S//REL) PREFER operational on DISHFIRE servers since January 2008, inserting content derived tags into xml output. First major utilization , SPYDER 2008 for selected content. SECRET//COMINT//REL TO USA, FVEY//20320108 6
    • SECRET//COMINT//REL TO USA, FVEY//20320108 (U) How Does PREFER Fi SECRET//SI//REL Discover e Targets DISHFIRE (SMS repository) Extract Useful Information Follow the Targe PREFER Extraction: -message recombination -" metadata" from content DISH FIRE SPYDER SEDB FASTCOPE Innovations SECRET//SI//REL SECRET//COMINT//REL TO USA, FVEY//20320108
    • ~ TOP SECRET//COMINT//REL TO USA, FVEY//20320108 (U//FOUO) PREFER Identification & Extraction April 2011 (S//51//REL) 194 Million Messages Collected by DISHFIRE per D Including • (S//SI//REL) VCARDS ~ names+; (113,672 average extracted daily sometimes DNIIink (email) to DNR (telephony) as well as images • (S//SI//REL) Geocoordinates (76, 142 daily avg; hex-encoded 1 - • • • • (S//SI//REL) (S//SI//REL) (S//SI//REL) (S//SI//REL) - • (12,809) Missed Calls ~ contact chaining (5,058, 114) SIM Card Changes ~ IMSI/IMEI links (6,017 ,901) Roaming information ~border crossings (1 ,658:025 Travel (5,314) Itinerary including multiple flights Changes: cancellations, reschedules, delays (S//SI//REL) Financial Transactions: - • Requests by people for route info Setting up meetings at a location Tracking information: e.g., Comma Separated Formats (33 ,020) Credit card transactions: correlate credit cards to individuals (61 ,488) Money transfers (social networks)- Phone to Phone (630,846) Track financial information (account activity- bank transaction) (115,480) (S//SI//REL) Passwords fnendina) ;_, Other Requests? TOP SEC'R ET//COM1N t //REL TO USA, FVEY//20320108 8