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Title:
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AN INCREMENTAL LEARNING SYSTEM FOR ONLINE KNN CLASSIFICATION: APPLICATION TO NETWORK INTRUSION DETECTION |
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Author(s):
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Ahmed Riadh Baba-Ali |
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ISBN:
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978-989-8533-80-7 |
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Editors:
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Ajith P. Abraham, Jörg Roth and Guo Chao Peng |
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Year:
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2018 |
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Edition:
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Single |
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Keywords:
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Incremental Learning, Classification, KNN, Network Intrusion Detection System |
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Type:
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Full Paper |
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First Page:
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27 |
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Last Page:
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34 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In this paper, we describe an incremental learning system based on the KNN classification algorithm (K Nearest Neighbor). Our approach uses an incremental learning paradigm since the used knowledge is improved over time, in contrast to the offline learning paradigm, where the learning is done once for all at the beginning. The incremental learning ability is attractive for problems with changing environments such as network intrusion detection where new and previously unknown attacks appear overtime. This kind of problems needs a continuous adaptation of the systems knowledge. Our system has been tested with live data coming from a real network under state of the art attacks. The results, showed that our approach is faster with a substantial smaller error rate and also a smaller false negative (FN) error rate compared to the classical KNN. |
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Digital Library 
