Debanjan Datta, Mohammad Raihanul Islam, Nathan Self, Naren Ramakrishnan


Developing algorithms that identify potentially illegal trade shipments is a non-trivial task, exacerbated by the size of shipment data as well as the unavailability of positive training data. In collaboration with conservation organizations, we develop a framework that incorporates machine learning and domain knowledge to tackle this challenge. Modeling the task as anomaly detection, we propose a simple and effective embedding-based anomaly detection approach for categorical data that provides better performance and scalability than the current state-of-art, along with a negative sampling approach that can efficiently train the proposed model. Additionally, we show how our model aids the interpretability of results which is crucial for the task. Domain knowledge, though sparse and scattered across multiple open data sources, is ingested with input of domain experts to create rules that highlight actionable results. The application framework demonstrates the applicability of our proposed approach on real world trade data. An interface combined with the framework presents a complete system that can ingest, detect and aid in the analysis of suspicious timber trades.

Debanjan Datta, Mohammad Raihanul Islam, Nathan Self, Amelia Meadows, John Simeone, Willow Outhwaite, Chen Hin Keong, Amy Smith, Linda Walker, Naren Ramakrishnan: Detecting Suspicious Timber Trades. AAAI 2020: 13248-13254


Naren Ramakrishnan

Nathan Self

Publication Details

Date of publication:
April 3, 2020
AAAI Conference on Artificial Intelligence
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