Application of Relevance Vector Machines in Real Time Intrusion Detection
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2012, Vol 3, Issue 9
Abstract
In the recent years, there has been a growing interest in the development of change detection techniques for the analysis of Intrusion Detection. This interest stems from the wide range of applications in which change detection methods can be used. Detecting the changes by observing data collected at different times is one of the most important applications of network security because they can provide analysis of short interval on global scale. Research in exploring change detection techniques for medium/high network data can be found for the new generation of very high resolution data. The advent of these technologies has greatly increased the ability to monitor and resolve the details of changes and makes it possible to analyze. At the same time, they present a new challenge over other technologies in that a relatively large amount of data must be analyzed and corrected for registration and classification errors to identify frequently changing trend. In this research paper an approach for Intrusion Detection System (IDS) which embeds a Change Detection Algorithm with Relevance Vector Machine (RVM) is proposed. IDS are considered as a complex task that handles a huge amount of network related data with different parameters. Current research work has proved that kernel learning based methods are very effective in addressing these problems. In contrast to Support Vector Machines (SVM), the RVM provides a probabilistic output while preserving the accuracy. The focus of this paper is to model RVM that can work with large network data set in a real environment and develop RVM classifier for IDS. The new model consists of Change Point (CP) and RVM which is competitive in processing time and improve the classification performance compared to other known classification model like SVM. The goal is to make the system simple but efficient in detecting network intrusion in an actual real time environment. Results show that the model learns more effectively, automatically adjust to the changes and adjust the threshold while minimizing the false alarm rate with timely detection.
Authors and Affiliations
Naveen C, Dr Natarajan. S, Dr Srinivasan. R
Keywords
Related Articles
- EP ID EP150976
- DOI -
- Views 64
- Downloads 0
Permutation of Web Search Query Types for User Intent Privacy
Privacy remains a major concern when using search engines to find for information on the web due to the fact that search engines own massive resources in preserving search logs of each user and organizations. However, ma...
Security Issues of a Recent RFID Multi Tagging Protocol
RFID is now a widespread method used for identifying people and objects. But, not all communication protocols can provide the same rigorous confidentiality to RFID technology. In return, unsafe protocols put individuals...
Secured Wireless Communication using Fuzzy Logic based High Speed Public-Key Cryptography (FLHSPKC)
In this paper secured wireless communication using fuzzy logic based high speed public-key cryptography (FLHSPKC) has been proposed by satisfying the major issues likes computational safety, power management and restrict...
A Novel Image Encryption using Memetic Differential Expansion based Modified Logistic Chaotic Map
Under this paper, the primary conditions of a modified logistic chaotic map are created with the help of memetic differential expansion. In the beginning, the color image is broken down into different channels like red,...
A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II
Diabetes is one of the major health problems as it causes physical disability and even death in people. Therefore, to diagnose this dangerous disease better, methods with minimum error rate must be used. Different models...