NDLI: Predictive modeling for intrusions in communication systems using GARMA and ARMA models

Content Provider	IEEE Xplore Digital Library
Author	Pillai, T.R. Abdullah, A. Palaniappan, S. Imran, H.M.
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. & IT, Taylors Univ., Subang Jaya, Malaysia (Abdullah, A.) \|\| Dept. of IT, Malaysia Univ. of Sci. & Technol., Petaling Jaya, Malaysia (Palaniappan, S.; Imran, H.M.) \|\| Fac. of Comput. & IT, Unitar Int. Univ., Petaling Jaya, Malaysia (Pillai, T.R.)
Abstract	The strength of time series modeling is generally not used in almost all current intrusion detection and prevention systems. By having time series models, system administrators will be able to better plan resource allocation and system readiness to defend against malicious activities. In this paper, we address the knowledge gap by investigating the possible inclusion of a statistical based time series modeling that can be seamlessly integrated into existing cyber defense system. Cyber-attack processes exhibit long range dependence and in order to investigate such properties a new class of Generalized Autoregressive Moving Average (GARMA) can be used. In this paper, GARMA (1,2; δ,1) model is fitted to cyber-attack data sets. Three different estimation methods are used to estimate the parameters. The Hannan-Rissanen Algorithm, Whittle Estimation Method and Maximum Likelihood Estimation methods are used to estimate the parameters of the GARMA (1,2;δ,1). Point forecasts to predict the attack rate possibly hours ahead of time also has been done and the performance of the models and estimation methods are discussed. The investigation of the case-study will confirm that by exploiting the statistical properties, it is possible to predict cyber-attacks (at least in terms of attack rate) with good accuracy. This kind of forecasting capability would provide sufficient early-warning time for defenders to adjust their defense configurations or resource allocations.
Starting Page	1
Ending Page	6
File Size	431208
Page Count	6
File Format	PDF
e-ISBN	9781479976263
DOI	10.1109/NSITNSW.2015.7176399
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-02-17
Publisher Place	Saudi Arabia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Computational modeling Generalized Autoregressive Moving Average Hidden Markov models Predictive models Intrusion forecasting Predictive modeling Communication Systems Forecasting Long range dependence Autoregressive processes
Content Type	Text
Resource Type	Article

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