CATS: Cluster-Aided Two-Step Approach for Anomaly Detection in Smart Manufacturing Dattaprasad Shetve, Raja VaraPrasad, Ramona Trestian, Huan X. Nguyen, and Hrishikesh Venkataraman Abstract In the age of smart manufacturing, there are typically multitude of sensors that are connected to each assembly line. The amount of data generated could be used to create a digital twin model of the complete process; wherein virtual replicas of the device and the process can be created before and during the process. An important aspect is automatic anomaly detection in the manufacturing process. Anomaly/outlier detection identifies data-points, events and/or observations that deviate from the dataset’s normal behaviour. A major problem in predicting anomaly from datasets is the limited accuracy that can be achieved. Several state-of-the-art techniques provide very high accuracy (>95%). However, these result in a considerable increase in the required time, thereby limiting its use to non-real-time applications. This paper pro- poses a cluster-aided two-step (CATS) approach for anomaly detection wherein two unsupervised detection techniques are employed in serial. The technique used for the first step is density-based spatial clustering for applications with noise (DBSCAN), while the second technique is local outlier factor (LOF). The output of the first-step technique is fed to the second technique, thereby utilizing the knowledge generated in the first step. An extensive simulation analysis indicates that the proposed CATS algorithm results in >95% accuracy for the outlier population is above 15% with a prediction time of lesser than 85 s. Keywords Anomaly · Clusters · Two-step · Smart manufacturing D. Shetve (B ) · R. VaraPrasad · H. Venkataraman Indian Institute of Information Technology, Sri City, India e-mail: dattaprasad.s@iiits.in R. VaraPrasad e-mail: yrv.prasad@iiits.in H. Venkataraman e-mail: hvraman@iiits.in R. Trestian · H. X. Nguyen School of Computing, Middlesex University, London, UK e-mail: r.trestian@mdx.ac.uk H. X. Nguyen e-mail: h.nguyen@mdx.ac.uk © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. M. Thampi et al. (eds.), Advances in Computing and Network Communications, Lecture Notes in Electrical Engineering 736, https://doi.org/10.1007/978-981-33-6987-0_9 103