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Pre-Crime Data Mining
1.1
Behavioral Profiling
With every call you make on your cell phone and every swipe of your debit and credit card a digital signature of when, what, and where you call and buy is incrementally built every second of every day in the servers of your credit card provider and wireless carrier. Monitoring the digital signatures of your consumer DNA-like code are models created with data mining technologies, looking for deviations from the norm, which once spotted instantly issue silent alerts to monitor your card or phone for potential theft. This is nothing new; it has been taking place for years. What is different is that since 9/11 this use of data mining will take an even more active role in the areas of criminal detection, security and behavioral profiling.
Behavioral profiling is not racial profiling, which is not only illegal, but a crude and not very effective process. Racial profiling simply does not work; race is simply too broad a category to be useful, it is one-dimensional. What is important however is suspicious behavior and the related digital information found in diverse databases, which data mining can be used to analyze and quantify. Behavioral profiling is the capability to recognize patterns of crimi-nal activity, to predict when and where the probabilities of crimes are likely to take place and to identify its perpetrators. Pre-crime is not science fiction; it is the objective of data mining techniques based on artificial intelligence (AI) technologies.
The same data mining technologies that have been used by marketers to provide ‘personalization’ which is the exact placement of the right offer, to the right person at the right time can be used for providing the right inquiry to the right perpetrator at the right time: before they commit the crime. Investi-gative data mining is the visualization, organization, sorting, clustering, seg-menting and prediction of criminal behavior using data attributes such as age, previous arrests, modus operandi, type of building, household income, time
2 1.2 Rivers of Scraps
of day, geo code, countries visited, housing type, auto make, length of resi-dency, type of license, utility usage, IP address, VISA type, number of chil-dren, place of birth, average usage of ATM card, number of credit cards, etc., the data points can run into the hundreds. Pre-crime is the interactive process of predicting criminal behavior by mining this vast array of data using several artificial intelligence technologies, including:
Link Analysis for creating graphical networks to view criminal associa-tions and interacassocia-tions
Intelligent Agents for retrieving, monitoring, organizing and acting on case related information
Text Mining for searching through gigabytes of documents in search of concepts and key words
Neural Networks for recognizing the patterns of criminal behavior and anticipating criminal activity
Machine Learning Algorithms for extracting rules and graphical maps of criminal behavior and perpetrator profiles
1.2
Rivers of Scraps
“It’s not going to be a cruise missile or a bomber that will be the determining fac-tor,” Defense Secretary Donald Rumsfeld said over and over in the days fol-lowing September 11. “It’s going to be a scrap of information.” Make that multiple scraps, millions of them flowing in a digital river of information at the speed of light from servers networked across the planet. Rumsfeld is right, the landscape of battle has irretrievably changed forever and so have the weap-ons, if commercial airliners can become missiles, so also how we use one of the most ethereal technologies of all human creativity and imagination: AI.
AI in the form of text mining robots scanning and translating terabyte databases able to detect deception, 3-D link analysis networks correlating human associations and interpersonal interactions, biometric identification devices monitoring for suspected chemicals, powerful pattern recognition neural networks looking for the signature of fraud, silent intrusion detection systems monitoring keystrokes, autonomous intelligent agent software retriev-ing e-mails able to sense emotions, real-time machine-learnretriev-ing profilretriev-ing sys-tems sitting in chat rooms – all bred from (and fostering) a new type of alien intelligence. These are the weapons and tools for criminal investigations of today and tomorrow, whether we like it or not.
Which of the 1.5 million people who cross U.S. borders each day is the courier for a smuggling operation? What respected merchant in ebay.com is
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about to abandon successful auction bidders, skipping out with hundred of thousands of dollars? What tiny shred of the world’s $1.5 trillion in daily for-eign exchange transactions is the payment from an al-Quaida cell for a loose Russian nuke? How many failed passwords attempts to log into a network is a sign of organized intrusion attack? Finding the needles in these type of mov-ing haystacks and answers to these kinds of questions is where data minmov-ing can be used to anticipate crimes and terrorist attacks.
1.3
Data Mining
Data mining is the fusion of statistical modeling, database storage, and artifi-cial intelligence technologies. Statisticians have been using computers for decades as a means to prove or disprove hypotheses on collected data. In fact one of the largest software companies in the world “rents” its statistical pro-grams to nearly every government agency and major corporation in the United States: SAS. Linear regressions and other types of modeling analyses are common and have been used in everything from the drug approval process by the Food and Drug Administration to the credit rating of individuals by financial service providers.
Another element in the development of data mining is the increasing abil-ity of data storage. In the 1970s, most data storage depended upon COBOL programs and storage systems not conducive to easy data extraction for induc-tive data analysis. Today however, organizations can store and query terabytes of information in sophisticated data warehouse systems. In addition, the development of multidimensional data models, such as those used in rela-tional database, has allowed users to move from a transaction view of custom-ers to a more dynamic and analytical way of marketing and retaining their most profitable clients.
However, the final element in data mining’s evolution is with AI, during the 1980s there was development of machine learning algorithms designed to enable software to learn, there were genetic algorithms designed to evolve and improve autonomously, and of course during that decade, neural networks came into acceptance as powerful programs for classification, prediction and profiling. During the last decade intelligent agents were developed able to autonomously incorporate all of these AI functions and use them to go out over networks and the Internet to scrounge the planet for information its mas-ters programmed them to retrieve. When combined, these AI technologies enable the creation of applications designed to listen, learn, act, evolve and identify anything from a potentially fraudulent credit card transaction to the detection of tanks from satellites, and of course now more then ever to pre-vent potential criminal activity.
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As a result of these developments, data mining flowered during the late 1990s, with many commercial, medical, marketing and manufacturing appli-cations. Retail companies eagerly applied complex analytical capabilities to their data to increase their customer base. The financial community found trends and patterns to predict fluctuations in stock prices and economic demand. Credit card companies used it to target their offerings, micro-seg-menting their customers and prospects maneuvering the best possible interest rates to maximize their profits. Telecommunication carriers used the technol-ogy to develop “churn” models, to predict which customers were about to jump ship and sign with their wireless competitor.
The ultimate goal of data mining is the prediction of human behavior, and is by far the most common business application, however this can easily be modified to meet the objective of detection and deterrence of criminals. These and many more application have demonstrated that, rather than requiring a human to attempt to deal with hundreds of descriptive attributes, data min-ing allows the automatic analysis of databases and the recognition of impor-tant trends and behavioral patterns.
Increasingly crime and terror in our world will be digital by nature. In fact one of the world largest criminal monitoring and detection enterprises in the world is at this very moment using a neural network to look for fraud. The HNC Falcon system uses, in part a neural network to look for patterns of potential fraud in about 80% of all credit card transactions every second of everyday. So it is that analysts and investigators will come to rely on machines and artificial intelligence to detect and deter crime and terrorism in today’s world. Breakthrough applications are already taking place in which neural networks are being used for forensic analysis of chemical compounds to detect arson and illegal drug manufacturing, coupled with agent technology, sensors can be deployed to detect bio-terror attacks, DARPA has already solicited a prototype for such a system.
1.4
Investigative Data Warehousing
Data warehousing is a practice of compiling transactional data with lifestyle demographics for constructing composites of customers and then de-compos-ing them via segmentation reports and data minde-compos-ing techniques – to extract profiles or “views” of who they are and what they value. Data warehouse niques have been practiced for a decade in private industry. These same tech-niques have so far not been applied to criminal detection and security deterrence, however they well could.
Using the same approach behavioral data from diverse sources such as the Internet (clickstream data captured by Internet mechanisms, such as cookies,
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invisible graphics, registration forms), demographics from data providers such as ChoicePoint, CACI, Experian, Acxiom, DataQuick, etc., utility and tele-com usage data, coupled with criminal data could be used to construct tele- com-posites representing views of perpetrators enabling the analysis of similarities and traits which through data mining could yield predictive models for inves-tigators and analysts. As with private industry better “views” of perpetrators could be developed enabling the detection and prevention of criminal and ter-rorist activity.
1.5
Link Analysis
Effectively combining multiple sources of data can lead law enforcement investigators to discover patterns to help them be proactive in their investiga-tions. Link analysis is a good start in mapping terrorist activity and criminal intelligence by visualizing associations between entities and events. Link anal-yses often involve seeing via a chart or a map the associations between suspects and locations whether by physical contacts, communications in a network, thru phone calls, financial transactions, or via the Internet and e-mail. Crim-inal investigators often use link analysis to begin to answer such questions as
“who know whom and when and where have they been in contact?”
Intelligence analysts and criminal investigators must often correlate enor-mous amounts of data about individuals in fraudulent, political, terrorist, nar-cotics and other criminal organizations. A critical first step in the mining of this data is viewing it in terms of relationships between people and organiza-tions under investigation. One of the first tasks in data mining and criminal detection involves the visualization of these associations, which commonly involves the use of link analysis charts.
