We investigated several semi-supervised anomaly detection methods like One Class SVM , Isolation forest , and Autoencoder . In all these methods a score is associated with each datapoint, which is then used to label the point as anomaly or normal class. We have observed that Autoencoder works best for our system. The details about anomaly detection using One class SVM and isolation forest is given in supplementary information. The choice of evaluation metric plays an important role in Machine learning problems. Anomaly detection is no different. In fact, more so since using a commonly used classification metric like accuracy might be counterproductive given the large imbalance of datasets for anomaly detection. Therefore we use F1 score and Precision for our model, formulas for the same are given below.