Business Process Anomaly Detection(BPAD) is turning out to be a really interesting topic, so decided to create a separate page for it and curate all the resources related to it here.

1. Books, Texts, Literature Reviews, Surveys

This is all the surveys/lit-reviews I could find on BPAD. And the latest one listed here is not available for public. I have a feeling BPAD needs a good, comprehensive literature review.

2024, Jan – Survey and Benchmark of Anomaly Detection in Business Processes
2023, Mar – A Systematic Review of Anomaly Detection for Business Process Event Logs
2017 – Systematic Literature Review on Anomaly Detection in Business Process Runtime Behavior
2017 – Anomaly Detection in Business Process Runtime Behavior - Challenges and Limitations
2008 – Anomaly Detection using Process Mining: by Prof. Will van der Aalst

2. Papers, Research

Plan to list down good number of papers which have had significant contributions. Along with that, I am looking at modern BPAD methods that involve the use of machine learning, deep learning, artificial intelligence, so the list will be crowded with that.

2025 – Is the real-time data of process safety reliable? An anomaly detection method based on the graph neural network
2025 – Multi-Graph Anomaly Detection in Business Process with Scalable Neural Architectures
2025, April – DABL: Detecting Semantic Anomalies in Business Processes using Large Language Models
2025 – Leverating GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes
2024, Dec – Enhancing Business Process Anomaly Detection with Neo4j and Graph Neural Networks
2024, Sep – GAMA: A multi-graph-based anomaly detection framework for business processes via graph neural networks
2024, Apr – Detecting Anamolous Events in Object-Centric Business Processes via Graph Neural Networks
2024 – Business Process Anomaly Detection and Root Cause Analysis using BLSTM-VAE With Attention
2024 – Using Deep Learning for Anomaly Detection in Business Process Logs
2024 – Business Process Management Anomaly Detection Through Semantic Embedding-Integrated Graph Neural Networks
2024 – Multi-Aspect Anomaly Detection with Graph Neural networks and Kolmogorov-Arnold Networks in Business Process Management
2024 – Edge Information Assisted Decoded for Business Process Anomaly Detection
2023, Oct – GRASPED: A GRU-AE Network Based Multi-Perspective Business Process Anomaly Detection Method
2023, September – Does This Make Sense? Machine Learning-Based Detection of Semantic Anomalies in Business Processes
2023, March – Deep Reinforcement Learning for dat-efficient weakly supervised business process anomaly detection
2022, Oct – PN-BBN: A Petri Net-Based Bayesian Network for Anomalous Behavior Detection
2022, July – Anomaly Detection for Service-Oriented Business Processes Using Conformance Analysis
2022 – Catch Me If You Can: Online Classification for Near Real-Time Anomaly Detection in Business Process Event Streams
2022 – Rethinking Graph Neural Networks for Anomaly Detection
2021 – Semi-Supervised Anomaly Detection in Business Process Event Data using Self-Attention based Classification
2021, Dec – Detecting temporal workarounds in business processes - A deep-learning based methods for analyzing event log data
2021, Oct – Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs
2021, Aug – Graph Autoencoders for Business Process Anomaly Detection
2021, July – A Novel Approach to Detect Anomalies in Business Process Event Logs using Deep Learning Algorithm
2020, Dec – Deep Learning Transformer Architecture for Predictive Business Processes Monitoring and Anomaly Detection
2020, Jan – Anomaly Detection in business processes using process mining and fuzzy association rule learning
2019, Oct – Autoencoders for improving quality of process event logs
2019, Aug – Detecting anomalies in hybrid business process logs
2019, Apr – An anomaly detection technique for business processes based on extended dynamic bayesian networks
2019, Feb – BINet: Multi-perspective Business Process Anomaly Classification
2018, April – Analyzing business process anomalies using autoencoders
2018 – BINet: Multivariate business process anomaly detection using deep learning
2016, Sep – Unsupervised Anomaly Detection in Noisy Business Process Event Logs using Denoising Autoencoders
2014 – Temporal Anomaly Detection in Business Processes
2008 – Anomaly Detection using Process Mining: by Prof. Will van der Aalst
1999 – Activity Monitoring: Noticing Interesting Changes in Behavior
1987 – An Intrusion-Detection Model