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 500
- Downloads 23
BAT algorithm for Cryptanalysis of Feistel cryptosystems
Recent cryptosystems constitute an effective task for cryptanalysis algorithms due to their internal structure based on nonlinearity. This problem can be formulated as NP-Hard. It has long been subject to various attacks...
An Analysis of Archive Update for Vector Evaluated Particle Swarm Optimization
Multi-objective optimization problem is commonly found in many real world problems. In computational intelligence, Particle Swarm Optimization (PSO) algorithm is a popular method in solving optimization problems. An exte...
GA Based Selective Harmonic Elimination for Five-Level Inverter Using Cascaded H-bridge Modules
Multilevel inverters (MLI) have been commonly used in industry especially to get quality output voltage in terms of total harmonic distortion (THD). In addition, development in semiconductor technology and advanced modul...
Particle Swarm Optimization Based Approach for Location Area Planning in Cellular Networks
Location area planning problem plays an important role in cellular networks because of the trade-off caused by paging and registration signalling (i.e., location update). Compromising between the location update and the...
Rainfall Runoff Modelling Using Generalized Neural Network and Radial Basis Network
Rainfall runoff study has a wide scope in water resource management. To provide a reliable prediction model is of paramount importance. Runoff prediction is carried out using generalized regression neural network and rad...