Title: GARCH and ANN-based DDoS detection and filtering in cloud computing environment

Authors: B.B. Gupta; Omkar P. Badve

Addresses: Department of Computer Engineering, National Institute of Technology Kurukshetra, India ' Department of Computer Engineering, National Institute of Technology Kurukshetra, India

Abstract: Nowadays, distributed denial-of-service (DDoS) attack is one of the major security threats in cloud computing environment as it compromises the availability of the services and risks everything including financial loss, reputation and losing faith of the customers. In this paper, we have proposed a novel solution, which can detect DDoS attack traffic in cloud environment using chaos theory. To predict the network traffic state, nonlinear time series model [i.e., generalised autoregressive conditional heteroskedasticity (GARCH) model] is used as it can capture the long-range dependence (LRD) and long-tail distribution which is an important property of network traffic. Prediction error is calculated using the prediction made by GARCH model and actual traffic pattern. Filtering is carried out using back propagation artificial neural network (ANN) on the traffic that exceeds the certain limit specified by some threshold. In our proposed approach, threshold is calculated dynamically, which makes our approach platform independent.

Keywords: distributed denial-of-service; DDoS; cloud computing nonlinear time series model; GARCH model; artificial neural network; ANN.

DOI: 10.1504/IJES.2017.086721

International Journal of Embedded Systems, 2017 Vol.9 No.5, pp.391 - 400

Received: 24 Oct 2015
Accepted: 18 Dec 2015
Published online: 24 Sep 2017 *

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