The fight against terrorism &#8211; an analytics application
Terrorist activities are trending strongly across news channels and investigative operations. In this scenario, surely analytics can help fight terrorism by predicting terror attacks and identifying terrorist financing? Oddly enough, it was a lawyer who first advocated wielding the power of data mining and analytics for fighting terrorism – and not a technocrat or member of the IT or data mining intelligence community! According to Philip Bobbitt, one way to combat terrorism is to strengthen the “valuable tool” of data mining and analytics by extracting information from disparate sources, such as “terrorist watch lists, airline reservations, immigration records” and more. Stacking up analytics on this data makes it further possible to identify patterns to support the government intelligence framework. Patterns can point out possible terrorists or even predict terrorist activities, facilitating global efforts at keeping citizens and property safe. Albeit, it is six years since Philip Bobbitt first mooted the idea in his book Terror and Consent, yet the adoption has sadly been lacking in India. This post from IVY is aimed to bring to the forefront yet another avant-garde application of analytics, in the hope we can see students and professionals of analytics venture into this largely unexplored domain. Leveraging Analytics to identify terrorist financing, patterns and potential hotspots The realm of fraud analytics is a well explored area, with the ability to detect various types of fraudulent transactions, unnatural financial activities, systemic suspicious transactions and money laundering. When extended to analyzing trade-based money laundering across geographies, anomalies and patterns can be detected in global underground financial systems that are the conduits of terrorist financing. Data mining, analysis and visualization tools have the ability to gather, connect, track, analyse and distribute intelligence information and leads about terrorist activities. Moving beyond the prediction capabilities, integrated seamless systems can additionally provide notifications and alerts for preventive action. Areas of countering terrorism, using analytics Identifying money laundering activities used for terrorist financing Following the money trail of terrorist organizations / suspicious individuals Identifying hotspots of terrorist activities for effective countermeasures Correlating terrorist attacks with trends in geo-politics and money trails Identifying potential uprising and terrorist sponsored activities Predicting potential terrorist activities based on any /all of the above Analytical Techniques in play Data mining, sentiment analysis, text mining, machine learning techniques and predictive analytics are some of the methodologies being leveraged to identify and combat terrorism. The Memex program also produces instant search results for specific domains and tasks, like patterns in state-wide crime or linkages with a car used for terrorist activity, for mission-critical action. Data utilized Different Government, intelligence, and criminal databases; financial systems, social media and internet, are some of the key data that is mined and analyzed. Using advanced data analytics patterns can be identified for policy making and measures, predicting organized terrorist activities and cutting off channels of terrorist financing, or even deflecting proxy terrorism or uprising using social media analytics. Use cases A project titled Computational Analysis of Terrorist Groups makes an excellent case of how analytical techniques can be leveraged for combating anti-terrorism.  Developed by researchers at the University of Maryland, the model, known as Temporal-Probabilistic Rule System, helps predict terrorist attacks of a particular terror organisation. It used algorithms to parse mined data on 770 variables from 20 years of a terrorist organisation’s activities, updated monthly for computational analysis.  This helped establish an understanding of the factors that determined frequency of attacks, types of terror strikes used in different geopolitical situations, trends in proxy terrorist activities and other criteria. Algorithms were also used to determine conditions that could lead to attacks. The Los Angeles Police Department and the London’s Metropolitan Police Service use sophisticated data-analysis systems, “designed to identify and connect related pieces of intelligence to help officers deter and respond to terrorist attacks”. Using a hybrid relational and open text-search database paired with an intelligence engine that compresses data, simultaneously search of multiple databases are possible. Although the above are mere examples of how programs can be developed to effectively counter terrorism, there are unlimited ways in which private analytic firms and individuals working within the Government system, can use mining and analytic techniques to support the global fight against terrorism. The market as well as scope for such programs, is unlimited. Bottomline The ROI of using analytics for countering terrorism cannot be quantified. Terrorism is today an albatross around the neck of every Government, not to forget the trauma associated with terrorist attacks and deaths. Although GIS systems are being used for countering terrorism, there is plenty of opportunity to leverage the power of analytics for tactical strategic solutions to counter terrorism and provide public safety.
Terrorist activities are trending strongly across news channels and investigative operations. In this scenario, surely analytics can help fight terrorism by predicting terror attacks and identifying terrorist financing? Oddly enough, it was a lawyer who first advocated wielding the power of data mining and analytics for fighting terrorism – and not a technocrat or member […]
Terrorist activities are trending strongly across news channels and investigative operations. In this scenario, surely analytics can help fight terrorism
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["Go", "machine learning", "AI", "sentiment analysis", "R", "ML", "RAG", "Aim", "analytics", "predictive analytics"]
["AI", "machine learning", "ML", "analytics", "Aim", "RAG", "predictive analytics", "sentiment analysis", "R", "Go"]
Sangeeta Deogawanka is an independent consultant and a GIS professional, with a passion for Data, Analytics and GIS. As part of the IVY Professional School in-house team, she covers topics ranging from applications and new terminologies to skill development.