A hybrid framework based on neural network MLP and means clustering for intrusion detection system
Due to the widespread use of Internet and communication networks, in case a reliable and secure network plays a crucial role for information technology (IT) service providers and users. The hardness of network attacks, as well as their complexity, has also increased lately.High false alarm rate is...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | http://repo.uum.edu.my/12030/1/PID20.pdf http://repo.uum.edu.my/12030/ http://www.icoci.cms.net.my/proceedings/2013/TOC.html |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | Due to the widespread use of Internet and communication networks, in case a reliable and secure network plays a crucial role for
information technology (IT) service providers and users. The hardness of network attacks, as well as their complexity, has also increased lately.High false alarm rate is a big issue for majority of researches in this area.To overwhelm this challenge a hybrid learning approach is proposed, employing the combination of K-means clustering and Neural Network Multi-Layer Perceptron (MLP) classification. Concerning the robustness of
K-means method and MLP algorithms benefits, this research is the part of an effort to develop a hybrid information detection system (IDS) which is able to detect high percentage of novel attacks while keep the false alarm at
low rate.This paper provides the conceptual view and a general framework of the proposed system. |
|---|