Detection of Anomalies using Local Outlier Factor and Isolation Forest algorithm

Abstract

: Data generated from smart devices or applications are in time-series format, in which information is recorded for each specific time. Anomalies in log data refer to certain patterns or points in data that deviate from average data. Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behavior. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, financial fault prevention, and industrial automation. Efficiency of Local Outlier Factor Algorithm, Isolation Forest Algorithm is compared. Testing dataset is obtained from Indian Council of Medical Research (ICMR) and credit card company transactional data.