Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification
In recent times, Intrusion Detection systems (IDSs) incarnate the high network security. Anomaly-based intrusion detection techniques, that utilize algorithms of machine learning, have the capability to recognize unpredicted malicious. Unluckily, an essential provocation of this method is to maximiz...
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| Format: | Article |
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Asian Research Publishing Network (A R P N)
2014
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| _version_ | 1796972656315072512 |
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| author | Juma, Sundus Muda, Zaiton Yassin, Warusia |
| author_facet | Juma, Sundus Muda, Zaiton Yassin, Warusia |
| author_sort | Juma, Sundus |
| collection | UPM |
| description | In recent times, Intrusion Detection systems (IDSs) incarnate the high network security. Anomaly-based intrusion detection techniques, that utilize algorithms of machine learning, have the capability to recognize unpredicted malicious. Unluckily, an essential provocation of this method is to maximize accuracy, detection whereas minimize false alarm rate. This paper proposed a hybrid machine learning approach based on X-Means clustering and Random Forest classification called XM-RF in order to aforementioned drawbacks. X-Means clustering is utilized to gather whole data into congruent cluster based on their behaviour whereas Random Forest classifier is utilized to rearrange the misclassified clustered data to apropos group. The ISCX 2012 Intrusion Detection Evaluation is used as a model dataset. The experimental result pose that the proposed approach obtains better than other techniques, with the accuracy, detection and false alarm rates of 99.96%, 99.99%, and 0.2%, respectively. |
| first_indexed | 2024-03-06T08:31:34Z |
| format | Article |
| id | upm.eprints-35184 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T08:31:34Z |
| publishDate | 2014 |
| publisher | Asian Research Publishing Network (A R P N) |
| record_format | dspace |
| spelling | upm.eprints-351842015-12-31T02:12:13Z http://psasir.upm.edu.my/id/eprint/35184/ Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification Juma, Sundus Muda, Zaiton Yassin, Warusia In recent times, Intrusion Detection systems (IDSs) incarnate the high network security. Anomaly-based intrusion detection techniques, that utilize algorithms of machine learning, have the capability to recognize unpredicted malicious. Unluckily, an essential provocation of this method is to maximize accuracy, detection whereas minimize false alarm rate. This paper proposed a hybrid machine learning approach based on X-Means clustering and Random Forest classification called XM-RF in order to aforementioned drawbacks. X-Means clustering is utilized to gather whole data into congruent cluster based on their behaviour whereas Random Forest classifier is utilized to rearrange the misclassified clustered data to apropos group. The ISCX 2012 Intrusion Detection Evaluation is used as a model dataset. The experimental result pose that the proposed approach obtains better than other techniques, with the accuracy, detection and false alarm rates of 99.96%, 99.99%, and 0.2%, respectively. Asian Research Publishing Network (A R P N) 2014-10-20 Article PeerReviewed Juma, Sundus and Muda, Zaiton and Yassin, Warusia (2014) Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification. Journal of Theoretical and Applied Information Technology, 68 (2). pp. 249-254. ISSN 1992-8645; ESSN: 1817-3195 http://www.jatit.org/volumes/sixtyeighth2.php |
| spellingShingle | Juma, Sundus Muda, Zaiton Yassin, Warusia Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification |
| title | Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification |
| title_full | Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification |
| title_fullStr | Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification |
| title_full_unstemmed | Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification |
| title_short | Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification |
| title_sort | reducing false alarm using hybrid intrusion detection based on x means clustering and random forest classification |
| work_keys_str_mv | AT jumasundus reducingfalsealarmusinghybridintrusiondetectionbasedonxmeansclusteringandrandomforestclassification AT mudazaiton reducingfalsealarmusinghybridintrusiondetectionbasedonxmeansclusteringandrandomforestclassification AT yassinwarusia reducingfalsealarmusinghybridintrusiondetectionbasedonxmeansclusteringandrandomforestclassification |