Signature-based anomaly intrusion detection using integrated data mining classifiers
As the influence of Internet and networking technologies as communication medium advance and expand across the globe, cyber attacks also grow accordingly. Anomaly detection systems (ADSs) are employed to scrutinize information such as packet behaviours coming from various locations on network to fin...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/47759/ http://psasir.upm.edu.my/id/eprint/47759/1/Signature-based%20anomaly%20intrusion%20detection%20using%20integrated%20data%20mining%20classifiers.pdf |
| _version_ | 1848850898418139136 |
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| author | Yassin, Warusia Udzir, Nur Izura Abdullah, Azizol Abdullah @ Selimun, Mohd Taufik Zulzalil, Hazura Muda, Zaiton |
| author_facet | Yassin, Warusia Udzir, Nur Izura Abdullah, Azizol Abdullah @ Selimun, Mohd Taufik Zulzalil, Hazura Muda, Zaiton |
| author_sort | Yassin, Warusia |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | As the influence of Internet and networking technologies as communication medium advance and expand across the globe, cyber attacks also grow accordingly. Anomaly detection systems (ADSs) are employed to scrutinize information such as packet behaviours coming from various locations on network to find those intrusive activities as fast as possible with precision. Unfortunately, besides minimizing false alarms; the performance issues related to heavy computational process has become drawbacks to be resolved in this kind of detection systems. In this work, a novel Signature-Based Anomaly Detection Scheme (SADS) which could be applied to scrutinize packet headers' behaviour patterns more precisely and promptly is proposed. Integrating data mining classifiers such as Naive Bayes and Random Forest can be utilized to decrease false alarms as well as generate signatures based on detection results for future prediction and reducing processing time. Results from a number of experiments using DARPA 1999 and ISCX 2012 benchmark dataset have validated that SADS own better detection capabilities with lower processing duration as contrast to conventional anomaly-based detection method. |
| first_indexed | 2025-11-15T10:13:36Z |
| format | Conference or Workshop Item |
| id | upm-47759 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:13:36Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-477592016-07-15T03:42:46Z http://psasir.upm.edu.my/id/eprint/47759/ Signature-based anomaly intrusion detection using integrated data mining classifiers Yassin, Warusia Udzir, Nur Izura Abdullah, Azizol Abdullah @ Selimun, Mohd Taufik Zulzalil, Hazura Muda, Zaiton As the influence of Internet and networking technologies as communication medium advance and expand across the globe, cyber attacks also grow accordingly. Anomaly detection systems (ADSs) are employed to scrutinize information such as packet behaviours coming from various locations on network to find those intrusive activities as fast as possible with precision. Unfortunately, besides minimizing false alarms; the performance issues related to heavy computational process has become drawbacks to be resolved in this kind of detection systems. In this work, a novel Signature-Based Anomaly Detection Scheme (SADS) which could be applied to scrutinize packet headers' behaviour patterns more precisely and promptly is proposed. Integrating data mining classifiers such as Naive Bayes and Random Forest can be utilized to decrease false alarms as well as generate signatures based on detection results for future prediction and reducing processing time. Results from a number of experiments using DARPA 1999 and ISCX 2012 benchmark dataset have validated that SADS own better detection capabilities with lower processing duration as contrast to conventional anomaly-based detection method. IEEE 2014 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/47759/1/Signature-based%20anomaly%20intrusion%20detection%20using%20integrated%20data%20mining%20classifiers.pdf Yassin, Warusia and Udzir, Nur Izura and Abdullah, Azizol and Abdullah @ Selimun, Mohd Taufik and Zulzalil, Hazura and Muda, Zaiton (2014) Signature-based anomaly intrusion detection using integrated data mining classifiers. In: International Symposium on Biometrics and Security Technologies (ISBAST 2014), 26-27 Aug. 2014, Kuala Lumpur, Malaysia. (pp. 232-237). 10.1109/ISBAST.2014.7013127 |
| spellingShingle | Yassin, Warusia Udzir, Nur Izura Abdullah, Azizol Abdullah @ Selimun, Mohd Taufik Zulzalil, Hazura Muda, Zaiton Signature-based anomaly intrusion detection using integrated data mining classifiers |
| title | Signature-based anomaly intrusion detection using integrated data mining classifiers |
| title_full | Signature-based anomaly intrusion detection using integrated data mining classifiers |
| title_fullStr | Signature-based anomaly intrusion detection using integrated data mining classifiers |
| title_full_unstemmed | Signature-based anomaly intrusion detection using integrated data mining classifiers |
| title_short | Signature-based anomaly intrusion detection using integrated data mining classifiers |
| title_sort | signature-based anomaly intrusion detection using integrated data mining classifiers |
| url | http://psasir.upm.edu.my/id/eprint/47759/ http://psasir.upm.edu.my/id/eprint/47759/ http://psasir.upm.edu.my/id/eprint/47759/1/Signature-based%20anomaly%20intrusion%20detection%20using%20integrated%20data%20mining%20classifiers.pdf |