Intrusion detection by initial classification-based on protocol type Online publication date: Fri, 17-Mar-2017
by D. Ashok Kumar; S.R. Venugopalan
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 9, No. 2/3, 2017
Abstract: Increased use of computer networks, internet and online transactions pose higher risk of intrusions and protecting the information from the hackers/intruders is a new area in computers and network security. The major factors which affect intrusion detection are the system's detection rate and time required to detect intrusions. Many researchers have focused in this area and have used data mining techniques for detecting the intrusions. This paper proposes to classify the dataset initially based on 'protocol type' feature and the performance improvements over traditional way of considering the full data without initial classification. This paper does not advocate any techniques or algorithms, but establishes the fact that by splitting the dataset on Protocol Type feature enhances performance with respect to detection rate and time to build model for intrusion detection. In this study, the well-known KDD Cup 99 intrusion dataset has been tested with the proposed approach. The computational study reveals that the initial classification based on protocol type' attribute increases the performance with respect to rate of detection and time to build model.
Online publication date: Fri, 17-Mar-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org