Intrusion Detection Forecasting Using Time Series for Improving Cyber Defence
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2015, Vol 3, Issue 1
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, 1; 1, ±) model is fitted to cyber-attack data sets. Two different estimation methods are used. 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.
Authors and Affiliations
Azween Abdullah *| School of Computing and IT, Taylors University, Subang Jaya, Selangor, Malaysia, Thulasy Ramiah Pillai| School of Computing and IT, Taylors University, Subang Jaya, Selangor, Malaysia, Cai Long Zheng| Unitar International University, Petaling Jaya, Selangor, Malaysia, Vahideh Abaeian| School of Business, Taylors University, Subang Jaya, Selangor, Malaysia
Keywords
Related Articles
- EP ID EP765
- DOI -
- Views 401
- Downloads 23
Statistical Methods for Quantitatively Detecting Fungal Disease from Fruits’ Images
In this paper we have proposed statistical methods for detecting fungal disease and classifying based on disease severity levels. Most fruits diseases are caused by bacteria, fungi, virus, etc of which fungi are respons...
Fuzzy approach to estimate the demand and supply quantitative imbalance at the labor market of information technology specialists
This document considers the processes of modelling supply and demand interactions in the labour market for information technology experts (IT professionals) and management of their quantitative disparity at the macro lev...
The Usage of Artificial Neural Networks Method in the Diagnosis of Rheumatoid Arthritis
In this study, artificial neural networks (ANN) method is used for the diagnosis of rheumatoid arthritis in order to support medical diagnostics. For the diagnosis of rheumatoid arthritis, backpropagation algorithm was e...
Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum
This paper presents a linear quadratic optimal controller design for a nonlinear inverted pendulum. Linear Quadratic Regulator (LQR), an optimal control method, is usually used for control of the dynamical systems. Main...
Lossless Text Compression Technique with LSB Technique to Hide Secret Message inside an Image (CLSB)
This paper presents CLSB algorithm to improve and increase the security ofhiding message inside an image by using Least Significant Bit (LSB) method. This research attempts to improve the way has been introduced in [1],...