Machine learning for discovery analytics to support criminal investigations

Wilmuth Müller; Dirk Pallmer; Dirk Mühlenberg; Ioannis Loumiotis; Konstantina Remoundou; Pavlos Kosmides; Konstantinos Demestichas

doi:10.1117/12.2557541

21 April 2020 Machine learning for discovery analytics to support criminal investigations

Wilmuth Müller, Dirk Pallmer, Dirk Mühlenberg, Ioannis Loumiotis, Konstantina Remoundou, Pavlos Kosmides, Konstantinos Demestichas

Author Affiliations +

Proceedings Volume 11395, Big Data II: Learning, Analytics, and Applications; 1139504 (2020) https://doi.org/10.1117/12.2557541
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Over the last decades, criminal activities have progressively expanded into the information technology (IT) world, adding to the “traditional” criminal activities, ignoring political boundaries and legal jurisdictions. Building upon the possibilities of technologies like Big Data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support LEAs in their critical need to exploit all available resources and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions for information fusion and classification tools intended to support LEA’s investigations. The Person Fusion Tool will be responsible for finding in an underlying knowledge graph different person instances that refer to the same person and fuse these instances. The general approach, the similarity metrics, the architecture of the tool and design choices as well as measures to improve the efficiency of the tool will be presented. The tool for classifying money transfer transactions uses decision trees. This is due to a requirement of easy explainability of the classification results, which is demanded from the ethical and legal perspective of the MAGNETO project. The design of the tool, the selected implementation and an evaluation based on anonymized financial data records will be presented.

Citation Download Citation

Wilmuth Müller, Dirk Pallmer, Dirk Mühlenberg, Ioannis Loumiotis, Konstantina Remoundou, Pavlos Kosmides, and Konstantinos Demestichas "Machine learning for discovery analytics to support criminal investigations", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 1139504 (21 April 2020); https://doi.org/10.1117/12.2557541

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KEYWORDS

Machine learning

Data fusion

Analytics

Data conversion

Legal

Computer programming

Databases

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