Anomaly-based intrusion detection through K-means clustering and naives Bayes classification
Intrusion detection systems (IDSs) effectively balance extra security appliance by identifying intrusive activities on a computer system, and their enhancement is emerging at an unexpected rate. Anomaly-based intrusion detection methods, which employ machine learning algorithms, are able to identify...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
UUM College of Arts and Sciences, Universiti Utara Malaysia
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/41322/ http://psasir.upm.edu.my/id/eprint/41322/1/41322.pdf |
| _version_ | 1848849664375259136 |
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| author | Mohamed Yassin, Warusia Udzir, Nur Izura Muda, Zaiton Sulaiman, Md. Nasir |
| author_facet | Mohamed Yassin, Warusia Udzir, Nur Izura Muda, Zaiton Sulaiman, Md. Nasir |
| author_sort | Mohamed Yassin, Warusia |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Intrusion detection systems (IDSs) effectively balance extra security appliance by identifying intrusive activities on a computer system, and their enhancement is emerging at an unexpected rate. Anomaly-based intrusion detection methods, which employ machine learning algorithms, are able to identify unforeseen attacks. Regrettably, the foremost challenge of this method is to minimize false alarm while maximizing detection and accuracy rate. We propose an integrated machine learning algorithm across K-Means clustering and Naïve Bayes Classifier called KMC+NBC to overcome the aforesaid drawbacks. K-Means clustering is applied to labeling and gathers the entire data into corresponding cluster sets based on the data behavior, i.e. , i.e. normal and attack, while Naïve Bayes Classifier (NBC) is applied to reorder the misclassified clustered data into correct categories. Experiments have been performed to evaluate the performance of KMC+NBC and NBC against ISCX 2012 Intrusion Detection Evaluation Dataset. The result shows that KMC+NBC significantly improves the accuracy, detection rate up to 99% and 98.8%, respectively, while decreasing the false alarm to 2.2%. |
| first_indexed | 2025-11-15T09:53:59Z |
| format | Conference or Workshop Item |
| id | upm-41322 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:53:59Z |
| publishDate | 2013 |
| publisher | UUM College of Arts and Sciences, Universiti Utara Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-413222019-05-15T08:13:36Z http://psasir.upm.edu.my/id/eprint/41322/ Anomaly-based intrusion detection through K-means clustering and naives Bayes classification Mohamed Yassin, Warusia Udzir, Nur Izura Muda, Zaiton Sulaiman, Md. Nasir Intrusion detection systems (IDSs) effectively balance extra security appliance by identifying intrusive activities on a computer system, and their enhancement is emerging at an unexpected rate. Anomaly-based intrusion detection methods, which employ machine learning algorithms, are able to identify unforeseen attacks. Regrettably, the foremost challenge of this method is to minimize false alarm while maximizing detection and accuracy rate. We propose an integrated machine learning algorithm across K-Means clustering and Naïve Bayes Classifier called KMC+NBC to overcome the aforesaid drawbacks. K-Means clustering is applied to labeling and gathers the entire data into corresponding cluster sets based on the data behavior, i.e. , i.e. normal and attack, while Naïve Bayes Classifier (NBC) is applied to reorder the misclassified clustered data into correct categories. Experiments have been performed to evaluate the performance of KMC+NBC and NBC against ISCX 2012 Intrusion Detection Evaluation Dataset. The result shows that KMC+NBC significantly improves the accuracy, detection rate up to 99% and 98.8%, respectively, while decreasing the false alarm to 2.2%. UUM College of Arts and Sciences, Universiti Utara Malaysia 2013 Conference or Workshop Item NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/41322/1/41322.pdf Mohamed Yassin, Warusia and Udzir, Nur Izura and Muda, Zaiton and Sulaiman, Md. Nasir (2013) Anomaly-based intrusion detection through K-means clustering and naives Bayes classification. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28-30 Aug. 2013, Sarawak, Malaysia. (pp. 298-303). http://www.icoci.cms.net.my/proceedings/2013/PDF/PID49.pdf |
| spellingShingle | Mohamed Yassin, Warusia Udzir, Nur Izura Muda, Zaiton Sulaiman, Md. Nasir Anomaly-based intrusion detection through K-means clustering and naives Bayes classification |
| title | Anomaly-based intrusion detection through K-means clustering and naives Bayes classification |
| title_full | Anomaly-based intrusion detection through K-means clustering and naives Bayes classification |
| title_fullStr | Anomaly-based intrusion detection through K-means clustering and naives Bayes classification |
| title_full_unstemmed | Anomaly-based intrusion detection through K-means clustering and naives Bayes classification |
| title_short | Anomaly-based intrusion detection through K-means clustering and naives Bayes classification |
| title_sort | anomaly-based intrusion detection through k-means clustering and naives bayes classification |
| url | http://psasir.upm.edu.my/id/eprint/41322/ http://psasir.upm.edu.my/id/eprint/41322/ http://psasir.upm.edu.my/id/eprint/41322/1/41322.pdf |