GARCH and ANN-based DDoS detection and filtering in cloud computing environment Online publication date: Sun, 24-Sep-2017
by B.B. Gupta; Omkar P. Badve
International Journal of Embedded Systems (IJES), Vol. 9, No. 5, 2017
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Embedded Systems (IJES):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook