FORECASTING DISTRIBUTED DENIAL OF SERVICE ATTACK USING HIDDEN MARKOV MODEL
No Thumbnail Available
Date
2016
Authors
Afolorunso, A.A.
Abass, O.
Longe, H. O. D.
Adewole, A.P.
Journal Title
Journal ISSN
Volume Title
Publisher
LAUTECH Journal of Engineering and Technology
Abstract
consumes the system resources in terms of time, memory, and processors. This paper presents a proposed method for forecasting DDoS in networks. The proposed model employs hidden Markov model (HMM) to forecast DDoS attacks. The method uses the inherent characteristic features of DDoS to determine the observable states of the system.To avoid intractable computations, Kullback-Leibler divergence algorithm was employed to reduce the number of observable states to three. The proposed model is formulated and trained through experiments using DARPA 2000 data set and the preliminary resultsshows that the characteristic features of the DDoS and the entropy concept can be used to formulate an HMM to predict DDoS.
Description
Keywords
Distributed denial of service , Forecasting , Hidden Markov model , Kullback-Leibler divergence.
Citation
Afolorunso, A.A., Abass, O., Longe, H. O. D., Adewole, A.P. (2016). FORECASTING DISTRIBUTED DENIAL OF SERVICE ATTACK USING HIDDEN MARKOV MODEL. LAUTECH Journal of Engineering and Technology 10 (1) 2016: 41-54