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Intrusion detection based on K-means clustering and Naïve Bayes classification

Intrusion Detection System (IDS) plays an effective way to achieve higher security in detecting malicious activities for a couple of years. Anomaly detection is one of intrusion detection system. Current anomaly detection is often associated with high false alarm with moderate accuracy and detection...

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Main Authors: Muda, Zaiton, Mohamed Yassin, Warusia, Sulaiman, Md. Nasir, Udzir, Nur Izura
Format: Conference or Workshop Item
Language:English
Published: IEEE 2011
Online Access:http://psasir.upm.edu.my/id/eprint/68866/1/Intrusion%20detection%20based%20on%20K-means%20clustering%20and%20Na%C3%AFve%20Bayes%20classification.pdf
http://psasir.upm.edu.my/id/eprint/68866/
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spelling my.upm.eprints.688662019-06-11T01:41:47Z http://psasir.upm.edu.my/id/eprint/68866/ Intrusion detection based on K-means clustering and Naïve Bayes classification Muda, Zaiton Mohamed Yassin, Warusia Sulaiman, Md. Nasir Udzir, Nur Izura Intrusion Detection System (IDS) plays an effective way to achieve higher security in detecting malicious activities for a couple of years. Anomaly detection is one of intrusion detection system. Current anomaly detection is often associated with high false alarm with moderate accuracy and detection rates when it's unable to detect all types of attacks correctly. To overcome this problem, we propose an hybrid learning approach through combination of K-Means clustering and Naïve Bayes classification. The proposed approach will be cluster all data into the corresponding group before applying a classifier for classification purpose. An experiment is carried out to evaluate the performance of the proposed approach using KDD Cup'99 dataset. Result show that the proposed approach performed better in term of accuracy, detection rate with reasonable false alarm rate. IEEE 2011 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68866/1/Intrusion%20detection%20based%20on%20K-means%20clustering%20and%20Na%C3%AFve%20Bayes%20classification.pdf Muda, Zaiton and Mohamed Yassin, Warusia and Sulaiman, Md. Nasir and Udzir, Nur Izura (2011) Intrusion detection based on K-means clustering and Naïve Bayes classification. In: 7th International Conference on Information Technology in Asia (CITA 2011), 12-13 July 2011, Kuching, Sarawak. . 10.1109/CITA.2011.5999520
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Intrusion Detection System (IDS) plays an effective way to achieve higher security in detecting malicious activities for a couple of years. Anomaly detection is one of intrusion detection system. Current anomaly detection is often associated with high false alarm with moderate accuracy and detection rates when it's unable to detect all types of attacks correctly. To overcome this problem, we propose an hybrid learning approach through combination of K-Means clustering and Naïve Bayes classification. The proposed approach will be cluster all data into the corresponding group before applying a classifier for classification purpose. An experiment is carried out to evaluate the performance of the proposed approach using KDD Cup'99 dataset. Result show that the proposed approach performed better in term of accuracy, detection rate with reasonable false alarm rate.
format Conference or Workshop Item
author Muda, Zaiton
Mohamed Yassin, Warusia
Sulaiman, Md. Nasir
Udzir, Nur Izura
spellingShingle Muda, Zaiton
Mohamed Yassin, Warusia
Sulaiman, Md. Nasir
Udzir, Nur Izura
Intrusion detection based on K-means clustering and Naïve Bayes classification
author_facet Muda, Zaiton
Mohamed Yassin, Warusia
Sulaiman, Md. Nasir
Udzir, Nur Izura
author_sort Muda, Zaiton
title Intrusion detection based on K-means clustering and Naïve Bayes classification
title_short Intrusion detection based on K-means clustering and Naïve Bayes classification
title_full Intrusion detection based on K-means clustering and Naïve Bayes classification
title_fullStr Intrusion detection based on K-means clustering and Naïve Bayes classification
title_full_unstemmed Intrusion detection based on K-means clustering and Naïve Bayes classification
title_sort intrusion detection based on k-means clustering and naïve bayes classification
publisher IEEE
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/68866/1/Intrusion%20detection%20based%20on%20K-means%20clustering%20and%20Na%C3%AFve%20Bayes%20classification.pdf
http://psasir.upm.edu.my/id/eprint/68866/
_version_ 1643839329864253440
score 13.211869

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