By Troy Kitch-Oracle on May 11, 2015
The unique thing here is that the police officers were directed to the parking structure by a computer program that had predicted that car burglaries were especially likely there that day. This computer program, developed by PredPol, is based on models used for predicting aftershocks from earthquakes, a common occurrence here in California. The algorithms used generated projections about which areas and windows of time are at highest risk for future crimes.
The Innovative Hacker
Organizations struggle to mitigate threats due to the continuing evolution of hackers and their methods of attack. Since William T. Morris Jr. first introduced the infant internet to his Morris worm virus in 1988, organizations have been fighting tweakers, script kiddies, espionage, and organized crime. The problem is that every time a solution is advised, a new hack is created. It’s a never ending cycle, and unfortunately, the turnaround time for hackers is getting shorter and shorter. They are innovating and sharing their innovations with others, who in turn take advantage and increase the number of effective attacks.
Learning from the Past
According to the RAND Corporation's “Predictive Policing" study, there is strong evidence to support the theory that crime is statistically predictable. That’s because criminals tend to operate in their comfort zone. They commit the type of crimes that they’ve committed successfully in the past, generally close to the same time, location and methods.
There is a connection between physical crime and the cybercrime organizations face today. To explain this connection further, the RAND Corporation found that prediction-led policing is not just about making predictions; "but it is a comprehensive business process, of which predictive policing is a part.” That process is summarized here in order to explain the steps taken to analyze past information in order to prevent further criminal activity.
The Importance of Acquiring Good, Clean Data
This entire process hinges on the collection of data and the importance of that data to make predictions.
Organizations today have the data necessary to make these types of predictions. In fact, our systems are churning out this data all the time through system server logs, database audits, event logs and more. If crime is statistically predictable, and we have all evidence right there in front of us, then we need to collect and analyze it.
Of course, the future of predictive analytics and machine learning is much more than analyzing audit and log data and monitoring our databases, however, these two critical practices are important first steps to a comprehensive cybersecurity program.
- Volume or amount of content transfer, such as e-mail attachments or uploads
- Resource access patterns, such as logins or data repository touches
- Time-based activity patterns, such as daily and weekly habits
- Indications of job contribution, such as the amount of source code checked in by developers
- Time spent in activities indicative of job satisfaction or discontent