Validation of Chaos Hypothesis in NADA and Improved DDoS Detection Algorithm

Author

Xinya Wu ; Yonghong Chen

Author_Institution

Sch. of Commputer Sci. & Technol., Huaqiao Univ., Xiamen, China

Volume

17

Issue

12

fYear

2013

fDate

Dec-13

Firstpage

2396

Lastpage

2399

Abstract

With the growth of the Internet, one of the most common attacks comes in the form of a distributed denial of service attack. In this paper, we validate that the prediction error of network traffic is chaotic which is used in NADA algorithm and improve the DDoS detection algorithm based on NADA. Firstly, the paper discusses the use of the largest Lyapunov exponent to validate the chaos hypothesis. Secondly, we predict the network traffic by using an exponential smoothing model instead of the forecasting method used in NADA. Then we analyze the prediction error by using chaos theory and Back Propagation Neural Network. The experimental results conducted and discussed in the paper show that our method can detect DDoS attacks up to 98.04% accuracy.

Keywords

Internet; Lyapunov methods; backpropagation; chaotic communication; computer network security; neural nets; telecommunication traffic; Internet; Lyapunov exponent; NADA algorithm; back propagation neural network; chaos hypothesis validation theory; distributed denial of service attack; exponential smoothing model; forecasting method; improved DDoS detection algorithm; network traffic prediction error analysis; Chaos; Computational modeling; Computer crime; Prediction algorithms; Predictive models; Smoothing methods; Time series analysis; Distributed denial-of-service (DDoS); Lyapunov exponents; anomaly detection; chaos models; exponential smoothing model;

fLanguage

English

Journal_Title

Communications Letters, IEEE

Publisher

ieee

ISSN

1089-7798

Type

jour

DOI

10.1109/LCOMM.2013.102913.130932

Filename

6656083