Anomaly detection using machine learning techniques
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https://doi.org/10.26637/MJM0804/0139Abstract
Anomaly represents deviation from the normal behavior of an event. Detection of anomaly provides means to take appropriate countermeasures in various domains. Examples include detection of fraudulent transaction in banking or financial domain, detection of cyber-attacks in networking environment, detection of abnormal behavior of vital signs of patient in healthcare domain. Also, detection of anomalies with respect to time of arrival of data is a crucial in deciding the accomplishment of successful countermeasures. Selection of suitable algorithm or method for detection of anomaly is also equally important for successful detection of anomalies. In this paper it is proposed to compare the performance of two different algorithms, namely, Isolation Forest (unsupervised) and Random Forest (supervised) by varying the operating parameters of the algorithms. Experiment is carried out using benchmark dataset that belongs to healthcare domain. The data is preprocessed for missing values and then detection accuracy of algorithm is analyzed with respect to number of records. Results are discussed.
Keywords:
Anomaly detection, Random Forest, Isolation Forest, batch processing, healthcareMathematics Subject Classification:
Mathematics- Pages: 2144-2148
- Date Published: 01-10-2020
- Vol. 8 No. 04 (2020): Malaya Journal of Matematik (MJM)
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