Spatial-Temporal Event Analysis as a Prospective Approach for Signalling Emerging Food Fraud-Related Anomalies in Supply Chains

Abstract

One of the pillars on which food traceability systems are based is the unique identification and recording of products and batches along the supply chain. Patterns of these identification codes in time and place may provide useful information on emerging food frauds. The scanning of codes on food packaging by users results in interesting spatial-temporal datasets. The analysis of these data using artificial intelligence could advance current food fraud detection approaches. Spatial-temporal patterns of the scanned codes could reveal emerging anomalies in supply chains as a result of food fraud in the chain. These patterns have not been studied yet, but in other areas, such as biology, medicine, credit card fraud, etc., parallel approaches have been developed, and are discussed in this paper. This paper projects these approaches for transfer and implementation in food supply chains in view of future applications for early warning of emerging food frauds.

Anomaly Detection Module for Network Traffic Monitoring in Public Institutions

“…Threat detection methods based on anomaly detection seemed appropriate for this purpose, as they focus on identifying and assessing abnormal events and states [ 6 , 7 , 8 ]. At that time, research papers described anomaly detection techniques used, for example, to identify fraud [ 9 , 10 ], detect threats from internal users [ 11 ], or detect advanced DDoS attacks [ 12 , 13 ]. In the course of these studies, researchers adopted various approaches, such as statistical analysis, methods of classification and clustering, fuzzy sets, approximate inference, neural networks, and other hybrid techniques.…”