NDLI: A New Coupled Metric Learning for Real-time Anomalies Detection with High-Frequency Field Programmable Gate Arrays

Content Provider	IEEE Xplore Digital Library
Author	Jiang, Frank Luo, Dan
Copyright Year	2014
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.
Starting Page	1254
Ending Page	1261
File Size	752450
Page Count	8
File Format	PDF
e-ISBN	9781479942749
DOI	10.1109/ICDMW.2014.203
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Neural networks Intrusion detection Vectors Real-time systems Software Metric Learning; Field Programmable Gate Arrays; Netflow; Intrusion Detection Systems Field programmable gate arrays
Content Type	Text
Resource Type	Article

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