TY - JOUR
T1 - A hybrid novelty score and its use in keystroke dynamics-based user authentication
AU - Kang, Pilsung
AU - Cho, Sungzoon
N1 - Funding Information:
This work was supported by Grant no. R01-2005-000-103900-0 from the Basic Research Program of the Korea Science and Engineering Foundation, the Brain Korea 21 program in 2006–2009 and partially supported by the Engineering Research Institute of SNU. This work was also supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2007-357-D00276) and Seoul R&BD Program (TR080589). About the Author —PILSUNG KANG received B.Sc. in 2003 and is currently a Ph.D candidate in the Department of Industrial Engineering, College of Engineering, Seoul National University, Seoul, Korea. His research interests include instance-based learning, learning kernel machines, novelty detection, learning algorithms in class imbalance, and non-linear dimensionality reduction. He is also interested in a wide range of applications such as keystroke dynamics-based authentication, fault detection in manufacturing process, and customer relationship management. He has published a number of papers on related topics in international journals and conferences. About the Author —SUNGZOON CHO is a Professor in the Department of Industrial Engineering, College of Engineering, Seoul National University, Seoul, Korea. He received B.Sc and MS in Industrial Engineering from Seoul National University, Seoul, Korea and MS and Ph.D. in Computer Science from the University of Washington and University of Maryland, respectively. His research interests are data mining, machine learning, pattern recognition, and their applications in various areas such as response modeling and keystroke-based authentication. He published over 100 papers in journals and proceedings. He also holds a US patent and a Korean patent concerned with keystroke-based user authentication.
PY - 2009/11
Y1 - 2009/11
N2 - The purpose of novelty detection is to detect (novel) patterns that are not generated by the identical distribution of the normal class. A distance-based novelty detector classifies a new data pattern as "novel" if its distance from "normal" patterns is large. It is intuitive, easy to implement, and fits naturally with incremental learning. Its performance is limited, however, because it relies only on distance. In this paper, we propose considering topological relations as well. We compare our proposed method with 13 other novelty detectors based on 21 benchmark data sets from two sources. We then apply our method to a real-world application in which incremental learning is necessary: keystroke dynamics-based user authentication. The experimental results are promising. Not only does our method improve the performance of distance-based novelty detectors, but it also outperforms the other non-distance-based algorithms. Our method also allows efficient model updates.
AB - The purpose of novelty detection is to detect (novel) patterns that are not generated by the identical distribution of the normal class. A distance-based novelty detector classifies a new data pattern as "novel" if its distance from "normal" patterns is large. It is intuitive, easy to implement, and fits naturally with incremental learning. Its performance is limited, however, because it relies only on distance. In this paper, we propose considering topological relations as well. We compare our proposed method with 13 other novelty detectors based on 21 benchmark data sets from two sources. We then apply our method to a real-world application in which incremental learning is necessary: keystroke dynamics-based user authentication. The experimental results are promising. Not only does our method improve the performance of distance-based novelty detectors, but it also outperforms the other non-distance-based algorithms. Our method also allows efficient model updates.
KW - Incremental learning
KW - Keystroke dynamics-based user authentication
KW - Nearest-neighbor learning
KW - Novelty detection
KW - Topological relation
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U2 - 10.1016/j.patcog.2009.04.009
DO - 10.1016/j.patcog.2009.04.009
M3 - Article
AN - SCOPUS:67649401708
VL - 42
SP - 3115
EP - 3127
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
IS - 11
ER -