In our work, we propose an anomaly detection framework, for detecting anomalous transactions in business processes from transaction event logs. Such a framework will help enhance the accuracy of anomaly detection in the global Supply Chain, improve the multi-level business processes workflow in the Supply Chain domain, and will optimize the processes in the Supply Chain in terms of security and automation. In the proposed work, Ontology is utilized to provide anomaly classification in business transactions, based on crafted SWRL rules for that purpose. Our work has been evaluated based on logs generated from simulating a generic business process model related to a procurement scenario, and the findings show that our framework was able to detect and classify anomalous transactions form those logs.