Anomaly Detection Using Deep Learning With Modular Networks

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Author(s): Manu C. (Ramaiah Institute of Technology, India), Vijaya Kumar B. P. (Ramaiah Institute of Technology, India)and Naresh E. (Ramaiah Institute of Technology, India)
Copyright: 2019
Pages: 35
Source title: Handbook of Research on Deep Learning Innovations and Trends
Source Author(s)/Editor(s): Aboul Ella Hassanien (Cairo University, Egypt), Ashraf Darwish (Helwan University, Egypt)and Chiranji Lal Chowdhary (VIT University, India)
DOI: 10.4018/978-1-5225-7862-8.ch015

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Neural Networks

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Abstract

In daily realistic activities, security is one of the main criteria among the different machines like IOT devices, networks. In these systems, anomaly detection is one of the issues. Anomaly detection based on user behavior is very essential to secure the machines from the unauthorized activities by anomaly user. Techniques used for an anomaly detection is to learn the daily realistic activities of the user, and later it proactively detects the anomalous situation and unusual activities. In the IOT-related systems, the detection of such anomalous situations can be fine-tuned with minor and major erroneous conditions to the machine learning algorithms that learn the activities of a user. In this chapter, neural networks, with multiple hidden layers to detect the different situation by creating an environment with random anomalous activities to the machine, are proposed. Using deep learning for anomaly detection would help in enhancing the accuracy and speed.

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Anomaly Detection Using Deep Learning With Modular Networks

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