COMB: Interconnected Transformers-Based Autoencoder for Multi-Perspective Business Process Anomaly Detection

Wei Guan, Jian Cao, Yan Yao, Yang Gu, Shiyou Qian

Abstract: In business processes, anomalies are prevalent, arising from diverse factors, such as software malfunctions and operator errors. Detecting these anomalies is imperative, as it significantly influences not only the financial well-being of a business but also the dependability of event logs for subsequent analysis. However, existing deep business process anomaly detection approaches either lack effective temporal dependency modeling between events or encounter challenges associated with gradient vanishing. In this paper, we introduce COMB, which stands for an interConnected transfOrmers-based autoencoder for Multi-perspective Business process anomaly detection. Considering the interdependent nature of multi-perspectives, COMB leverages multiple parallel and interconnected transformers, facilitated by the inclusion of aggregation layers. These layers serve as integration points for information from various perspectives. Notably, considering how control flow influences other perspectives, COMB incorporates innovative mask adapters to enhance its detection performance. Furthermore, we propose a novel method for calculating anomaly scores, which effectively mitigates the influence of varying numbers of potential attribute values. Our extensive experimental evaluation encompasses both synthetic and real-life logs, and the results clearly demonstrate that COMB outperforms state-of-the-art methods in both trace-level and attribute-level anomaly detection.