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Adaptive Neuro-Fuzzy-Based Anomaly Detection System in Cloud

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Abstract

Despite its continuous improvement, cloud computing system is still vulnerable to malicious activities. This has necessitated in constructing an anomaly detection component for discovering anomalies in cloud environment. In this paper, an anomaly detection system at hypervisor layer named hypervisor detector is developed and evaluated to detect the malicious activities in cloud environment. Deployment of fuzzy systems in intrusion detection systems has the ability to detect the presence of uncertain and imprecise nature of anomalies in cloud environment. But they fail in constructing models based on target data. One of the successful approaches based on target data is integration of fuzzy systems with adaptation and learning proficiencies of neural network called adaptive neuro-fuzzy inference system (ANFIS) model. The hypervisor detector is designed and developed with ANFIS and is practiced with a hybrid algorithm, which is a combination of back propagation gradient descent technique with least square method. For experiments, DARPA’s KDD cup dataset is used. The performance analysis and results show that the proposed hypervisor detector based on ANFIS is well designed to detect the anomalies in cloud environment with minimum false negative rate and high detection accuracy.

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Authors and Affiliations

  1. Department of Information Technology, PSNA College of Engineering & Technology, Dindigul, Tamil Nadu, India

    P. Ganeshkumar & N. Pandeeswari

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  1. P. Ganeshkumar
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  2. N. Pandeeswari
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Correspondence to N. Pandeeswari.

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Ganeshkumar, P., Pandeeswari, N. Adaptive Neuro-Fuzzy-Based Anomaly Detection System in Cloud. Int. J. Fuzzy Syst. 18, 367–378 (2016). https://doi.org/10.1007/s40815-015-0080-x

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  • DOI: https://doi.org/10.1007/s40815-015-0080-x

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