Machine Learning Applications for Anomaly Detection

Machine Learning Applications for Anomaly Detection

Teguh Wahyono (Satya Wacana Christian University, Indonesia) and Yaya Heryadi (Bina Nusantara University, Indonesia)
Copyright: © 2019 |Pages: 35
DOI: 10.4018/978-1-5225-7955-7.ch003

Abstract

The aim of this chapter is to describe and analyze the application of machine learning for anomaly detection. The study regarding the anomaly detection is a very important thing. The various phenomena often occur related to the anomaly study, such as the occurrence of an extreme climate change, the intrusion detection for the network security, the fraud detection for e-banking, the diagnosis for engines fault, the spacecraft anomaly detection, the vessel track, and the airline safety. This chapter is an attempt to provide a structured and a broad overview of extensive research on anomaly detection techniques spanning multiple research areas and application domains. Quantitative analysis meta-approach is used to see the development of the research concerned with those matters. The learning is done on the method side, the techniques utilized, the application development, the technology utilized, and the research trend, which is developed.
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Background

Anomaly, also known as outliers, is a term refers to irregularity or deviation from the normal pattern (Chandola, et al., 2007). Yang (2007) refered the term anomaly to observation data that strongly inconsistent with the previous compiled data. Recently, Bloomquist (2015) defined anomaly as “patterns or data points that do not conform to a well defined notion of normal behaviour.”

Anomaly detection problem refers to the task of finding patterns in data that do not conform to expected behavior (Chandola, 2007). The problem is an interesting computer vision problem with many potential applications ranging from climate change detection, anomaly detection of fault tolerant robotic system (Jakimovski, 2011) to fraud transaction detection. In the past decade, anomaly detection problem has raised wide attention from various research domains due to its potential applications for recognizing indication that the underlying process that induces the data does not happen as expected. Depending on the context of the data, the detected anomalous data can be interpreted as either extreme climate change (Kawale, 2011), network security intrusion (Tsai, et al., 2010), medical diagnosis (Park, et al., 2015), engines fault (Djurdjanovic, et al., 2007), spacecraft anomaly detection (Fujimaki, et al., 2007), Mobility-Based Anomaly Detection in Cellular Mobile (Sun, et al., 2006) or vessel track and the airline safety diagnosis (Budalakoti et al., 2009).

Despite many studies have been reported, anomaly detection remained a challenging problem. A prominent study reported by (Chandola, et al., 2007) summarized several challenges in detecting anomaly as follows.

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