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Title: Network-traffic anomaly detection with incremental majority learning
Authors: 郁方
Huang, Shin-Ying
Yu, Fang
Tsaih, Rua-Huan
Huang, Yennun
Contributors: 資管系
Keywords: Computer crime;Data mining;Mercury (metal);Neural networks;Statistical tests;Adaptive modeling;Changing environment;Data abstraction;Essential features;Incremental learning;Intrusion Detection Systems;Outlier Detection;Training and testing;Intrusion detection
Date: 2015
Issue Date: 2017-08-09 17:28:34 (UTC+8)
Abstract: Detecting anomaly behavior in large network traffic data has presented a great challenge in designing effective intrusion detection systems. We propose an adaptive model to learn majority patterns under a dynamic changing environment. We first propose unsupervised learning on data abstraction to extract essential features of samples. We then adopt incremental majority learning with iterative evolutions on fitting envelopes to characterize the majority of samples within moving windows. A network traffic sample is considered an anomaly if its abstract feature falls on the outside of the fitting envelope. We justify the effectiveness of the presented approach against 150000+ traffic samples from the NSL-KDD dataset in training and testing, demonstrating positive promise in detecting network attacks by identifying samples that have abnormal features. © 2015 IEEE.
Relation: Proceedings of the International Joint Conference on Neural Networks, 2015-September
Data Type: conference
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