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A Comparative Analysis of a Novel Anomaly Detection Algorithm with Neural Networks

A Comparative Analysis of a Novel Anomaly Detection Algorithm with Neural Networks
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Author(s): Srijan Das (National Institute of Technology, India), Arpita Dutta (National Institute of Technology, India), Saurav Sharma (National Institute of Technology, India) and Sangharatna Godboley (National Institute of Technology, India)
Copyright: 2020
Pages: 17
Source title: Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch004

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Abstract

Anomaly Detection is an important research domain of Pattern Recognition due to its effects of classification and clustering problems. In this paper, an anomaly detection algorithm is proposed using different primitive cost functions such as Normal Perceptron, Relaxation Criterion, Mean Square Error (MSE) and Ho-Kashyap. These criterion functions are minimized to locate the decision boundary in the data space so as to classify the normal data objects and the anomalous data objects. The authors proposed algorithm uses the concept of supervised classification, though it is very different from solving normal supervised classification problems. This proposed algorithm using different criterion functions has been compared with the accuracy of the Neural Networks (NN) in order to bring out a comparative analysis between them and discuss some advantages.

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