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Home > Press > Anomaly Detection Proof-of-Concept for Big Data Passenger Screening

Anomaly Detection Proof-of-Concept for Big Data Passenger Screening

Utrecht, February 17, 2020 - As part of ongoing research to enhance WCC Passenger Screening Solution HERMES, WCC developed a model for detecting anomalous patterns in big passenger data. Anomaly detection is a technique for identifying data points that do not follow the expected behavior of the full data set. Such anomalous data could point to critical incidents or potential opportunities.

WCC HERMES is a solution for Passenger Screening that offers extensive investigation capabilities. HERMES enables border security experts to create custom rules for targeting specific suspicious patterns, such as ‘male age 30-55 traveling with multiple females with different surnames age <20’. Anomaly detection could prove of great value in identifying out-of-the-ordinary passenger behaviors based on parameters such as travel itinerary, payment method, and origin.

WCC Data Science Department created a Proof-of-Concept using a data set including age, nationality, origin, destination and type of passenger. After training the model, it can be run against real passenger lists to identify anomalies for specific flights. The successful Proof-of-Concept will enable the addition of new investigative capabilities to WCC HERMES.