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 487
- Downloads 23
Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks
Quality is one of the important factors in agricultural products marketing. Grading machines have great role in quality control systems. The most efficient method used in grading machines today is image processing. This...
A Note on Entropy Subsethood Relationship
We comment on subsethood measure defined by Kosko and Young and give some new aspects of these measures. Finally we would like to discard the entropy subsethood relationship established by the authirs. We present some pr...
Predicting Student Success in Courses via Collaborative Filtering
Based on their skills and interests, students’ success in courses may differ greatly. Predicting student success in courses before they take them may be important. For instance, students may choose elective courses that...
New Approach in E-mail Based Text Steganography
In this study combination of lossless compression techniques and Vigenere cipher was used in text steganography that makes use of email addresses to be the keys to reconstruct the secret message which has been embedded i...
SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal
Sleep apnea (SA) is the most commonly known sleeping disorder characterized by pauses of airflow to the lungs and often results in day and night time symptoms such as impaired concentration, depression, memory loss, snor...