Recognizing dangerous behaviors with predictive analytics and machine learning complements current airport security measures and helps create a safer environment from baggage claim to the runways.
Airports have continued to spend millions of dollars to implement smart security measures, including tighter security checkpoints, facial recognition software, full-body scanners, IoT-based access control systems, intrusion detection, alarms, video surveillance and increased security personnel.
Behavior recognition may soon contribute its part in airport security programs. For more than a decade, airports have made finding dangerous items their primary objective to protect passengers and crews. Those techniques may be joined soon by a more thorough cross check, possible by analyzing passenger behavior picked up by surveillance cameras and stored in passenger and airport data.
Current security solutions evaluate at a ‘single point in time’ rather than a summation of a person’s entire behavior over an extended period of time. The theory behind behavior recognition is that when someone is in the process of carrying out a criminal or terrorist act, that person exhibits behavior that is out of the norm. The behaviors can be split into two categories – micro behavior and macro behavior.
Photo credit: Intel Digital Security and Surveillance Solutions, Intel.in
Facial expressions, perspiration, lack of eye contact are micro examples. Macro behavior is broader movement throughout the space, such as attempting to hide his or her face by turning away when someone approaches; trying to stay out of sight, behind obstructions or shadows to avoid being seen; or leaving an area when the person in question believes he or she has been detected.
In the ideal scenario, airports would build a 360-degree view of each person. Data collected would be security screenings, behavior tracking, information from other sources such as bookings, travel history and so on. By applying predictive analytics and reviewing these large sets of structured and unstructured data, airport security would grade each person on their risk potential.
A 360-Degree View of Behavior
Tracking the full set of passenger data sets has been too expensive and difficult—until now. Recent analytics advancements make it possible to build a 360-degree view of passenger activities. Add in rich graphical interfaces, mapping tools, and geolocation information, and the security team has the resources and insight to understand which passengers in crowded airports are likely security threats.
Ironically, technology is often better than humans are at recognizing atypical human behavior. We’ve trusted security, staff, or another traveler to spot something out of the ordinary, but what is most difficult for staff is tracking all the acts that combine to denote terrorist behaviors. Each of these acts on its own may not raise suspicion. It’s only when the security team can see behavior end-to-end that they can get a complete view.
IoT technology such as smart cameras in the terminals and door locks for authorized airport personnel can offer a complete view, from parking lot to boarding to baggage handling to the runway. Once predictive analytics identify an individual as high risk, the security team can request a private interview to find out if they need to investigate further.
While threats can come from outside sources such as terrorists, drug smugglers or passengers, they can also be instigated from within, planted by a disgruntled airport employee, vendor/tenant or contract worker. By using predictive analytics, security operations managers can monitor both access and behavior of internal employees and contractors, identifying dangerous insiders and halting an attack before it happens.
By gathering Big Data from IoT cameras, sensors and locks and using predictive analytics, the security ops team can automatically monitor and note suspicious behavior or irregular employee movements. For example, is an employee assigned to one area of a terminal using a card key trying to enter another terminal? Is a baggage handler at the airport entering a restricted area on their assigned day off?
Using predictive analytics, the security team can correlate data sets on employees from disparate sources and analyze blended threats. The team can use analytics to track: HR flags, such as an employee who has a history of performance issues; personal data such as criminal records; information system access including on-site VPN usage; and physical movement within the airport terminals from badge scans or IoT-based door locks and geo-spatial scanners.
Machine Learning platforms can process these large data sets and tie together and connect the dots across multiple behaviors and employee actions. Using in-memory computing, the security team has the speed capabilities—minutes vs. days—needed to use analytics and determine a real-time response that can prevent internal incidents from happening.
With a combination of IoT technology, behavior recognition and predictive analytics, airport security teams can continuously monitor the airport environment. If they can connect the dots across multiple actions, they can use the data and analyze risk in real-time, preventing malicious threats before they occur.
Learn more about gathering real-time analytics with the Intel IoT platform and SAP.