The week's pick
Reseach Article
Hybrid Approach of Intrusion Detection based on Sequential Feature Selection, EM Clustering and Decision Stump Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 148 - Number 9 |
| Year of Publication: 2016 |
| Authors: Gulshan Ansari, Tanveer Farooqui |
10.5120/ijca2016911280
|
Gulshan Ansari, Tanveer Farooqui . Hybrid Approach of Intrusion Detection based on Sequential Feature Selection, EM Clustering and Decision Stump Classification. International Journal of Computer Applications. 148, 9 ( Aug 2016), 13-18. DOI=10.5120/ijca2016911280
Abstract
Other than the emerging IT sector, security is still being a major issue for various companies. Various companies are suffering from several types of threats these days like viruses or intrusions, etc. There are various types of techniques have been applied by the companies like for detection of intrusion and also for providing prevention to the system in order to secure the companies against these types of intrusions. The technology of Intrusion-detection can have a lot of problems, such as low performance, low intelligent level, and more false-negative-rate, high-false-alarm-rate, and so on. The purpose of the IDS is to detect attacks. Objective of this analysis is to describe the phases of the development of concepts of the IDS along with its significance for the researchers and the research-centers, military domains, security and also in order to determine the significance of IDS categories, its classifications, and in which area applied the IDS in order to decrease the threats of network.
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