In the wake of recent terror attacks in San Bernardino, Paris and Brussels, world leaders are scrambling to find new ways to curb these acts of violence. What has changed since 9/11 is the crowd sourcing nature of these attacks with the use of the internet and mobile phones. One of the tools for combating terrorism has been implementing measures to deprive terrorists of funding. However, the new crowd sourcing nature of of recent attacks have changed the tactics required to identify potential terrorist activities and deny them funding.
After 9/11, the US implemented the Terrorist Finance Tracking Program (TFTP), to follow the money trail that enabled the terrorist to train in flight schools, and live in the U.S. for an extended period of time. TFTP enabled the CIA to track international fund transfers, paying particular focus on Islamic charities. Financial institutions were also required to implement stronger Anti-Money Laundering Initiatives under the Patriots Act.
These financial measure have little effect on denying funding for crowd sourced Jihad. The terrorists in San Bernardino, Paris, and Brussels were self funded or funded by supporters in country, using smaller sums of money that would not be detected by money laundering or TFTP.
Critics of Silicon Valley from the national-security community are charging that technology like crypto currency and encrypted cell phones are enabling terrorists to operate undetected. Apple’s refusal to de-encrypt the San Bernardino terrorist’s cell phone has resulted in the Burr-Feinstein encryption bill, under consideration in congress, that would require technology firms to decrypt customer’s data at the court’s request. China has already passed an Anti-Terrorism law that requires decryption on demand. Others are requiring controls on crypto currencies like Bitcoin.
These same technologies, however, could help the intelligence community thwart those plotting against us and our allies. The technology behind Bitcoin is Blockchain which replaces the traditional central ledger with a distributed ledger. Blockchain is an authenticated ledger that records digital transactions, but is increasingly used for validating all types of records like corporate registry information which is currently siloed by local and off shore jurisdictions. Blockchain would help governments manage business data and identify firms and individuals engaged in illicit activity.
Moyara Ruehsen, points out in a recent News Week article that:
“ISIS-affiliated cells in Europe are typically self-funded. Apart from any training the attackers may have had in Syria, the attacks required minimal funding, such as daily expenses and cash to purchase weapons on the black market.
Most of the terrorists were legal residents or citizens of the EU, and they were presumably able to raise most of these funds on their own within the EU. Indeed, European law enforcement has found that most of these homegrown cells finance their operations with petty crime including drug dealing, credit card fraud and forgery.
However, even if these cells are receiving minimal funds from abroad, they are still likely to leave financial footprints within the EU. They may withdraw funds from an ATM, use a money service business to wire cash to another city, or even pay cash to set up a post office box.”
All these transactions leave an electronic or video footprint, where we can use the technology of today to ingest, correlate, visualize, and apply pattern recognition and predictive analytics to track and thwart terrorist activities.
Money Laundering technologies are focused on structured data and data warehouses that are run in financial institutions. More and more transactions are being done out side of the banks and the data is unstructured, provided by cameras, sensors, and click streams. Data lakes can replace data warehouses and correlate structured and unstructured data to provide a 360 view of activities. Advanced machine learning tools can identify patterns that can’t be discerned through traditional algorithms. Instead of an analyst feeding a search engine with clues, Machine learning tools like Hitachi Visualization Predictive Crime Analytics are capable of absorbing massive amounts of data and learning on the fly. New similar face technology from Hitachi, uses edge pattern and clustering technology to scan 32 million faces in a second and can match that face at an ATM with a photo in a terrorist database.
Instead of criticizing the technology companies and passing laws that may open up more security exposures, like back doors to encryption technologies, the security community should work with the technology community, become better educated on technologies that are available and recognize that the old ways of addressing terrorism have to keep up with changing terrorism methods such as crowdsourcing