A new coupled metric learning for real-time anomalies detection with high-frequency field programmable gate arrays

Jiang, F; Luo, D

A new coupled metric learning for real-time anomalies detection with high-frequency field programmable gate arrays

Jiang, F Luo, D

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Data Mining Workshop (ICDMW), 2014 IEEE International Conference on, 2014, pp. 1254 - 1261
Issue Date:: 2014

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Field	Value	Language
dc.contributor.author	Jiang, F	en_US
dc.contributor.author	Luo, D	en_US
dc.date	2014-12-14	en_US
dc.date.issued	2014	en_US
dc.identifier.citation	Data Mining Workshop (ICDMW), 2014 IEEE International Conference on, 2014, pp. 1254 - 1261	en_US
dc.identifier.isbn	978-1-4799-4275-6	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120696
dc.description.abstract	Billions of internet end-users and device to device connections contribute to the significant data growth in recent years, large scale, unstructured, heterogeneous data and the corresponding complexity present challenges to the conventional real-time online fraud detection system security. With the advent of big data era, it is expected the data analytic techniques to be much faster and more efficient than ever before. Moreover, one of the challenges with many modern algorithms is that they run too slowly in software to have any practical value. This paper proposes a Field Programmable Gate Array (FPGA) -based intrusion detection system (IDS), driven by a new coupled metric learning to discover the inter- and intra-coupling relationships against the growth of data volumes and item relationship to provide a new approach for efficient anomaly detections. This work is experimented on our previously published NetFlow-based IDS dataset, which is further processed into the categorical data for coupled metric learning purpose. The overall performance of the new hardware system has been further compared with the presence of conventional Bayesian classifier and Support Vector Machines classifier. The experimental results show the very promising performance by considering the coupled metric learning scheme in the FPGA implementation. The false alarm rate is successfully reduced down to 5% while the high detection rate (=99.9%) is maintained.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Data Mining Workshop (ICDMW), 2014 IEEE International Conference on	en_US
dc.relation.ispartof	IEEE International Conference on Data Mining	en_US
dc.relation.isbasedon	10.1109/ICDMW.2014.203	en_US
dc.rights	© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	A new coupled metric learning for real-time anomalies detection with high-frequency field programmable gate arrays	en_US
dc.type	Conference Proceeding
utslib.location.activity	Shenzhen; China	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.finish-date	2014-12-14	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2014-12-14	en_US

Abstract:

Billions of internet end-users and device to device connections contribute to the significant data growth in recent years, large scale, unstructured, heterogeneous data and the corresponding complexity present challenges to the conventional real-time online fraud detection system security. With the advent of big data era, it is expected the data analytic techniques to be much faster and more efficient than ever before. Moreover, one of the challenges with many modern algorithms is that they run too slowly in software to have any practical value. This paper proposes a Field Programmable Gate Array (FPGA) -based intrusion detection system (IDS), driven by a new coupled metric learning to discover the inter- and intra-coupling relationships against the growth of data volumes and item relationship to provide a new approach for efficient anomaly detections. This work is experimented on our previously published NetFlow-based IDS dataset, which is further processed into the categorical data for coupled metric learning purpose. The overall performance of the new hardware system has been further compared with the presence of conventional Bayesian classifier and Support Vector Machines classifier. The experimental results show the very promising performance by considering the coupled metric learning scheme in the FPGA implementation. The false alarm rate is successfully reduced down to 5% while the high detection rate (=99.9%) is maintained.

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http://hdl.handle.net/10453/120696