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• W2801309385 abstract "The intrusion detection for the network traffic is a technique to detect abnormal traffic flow patterns in periodic network packets. The traffic flooding attacks can be detected by the abnormal intrusion detection techniques that detects well known attack patterns. In this paper, we propose an intrusion detection way to classify normal and abnormal traffic packet pattern by converting traffic into time series data and analyzing them, and apply the information gain technique to reduce the learning execution times. That is, the normal and abnormal packet patterns are classified by applying the shapelets technique to the time-series pattern between the normal traffic and the abnormal traffic packet patterns. The experimental results show that the proposed method classifies normal patterns and traffic flooding attacks into 95% accuracy." @default.
• W2801309385 created "2018-05-17" @default.
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• W2801309385 date "2018-01-01" @default.
• W2801309385 modified "2023-09-25" @default.
• W2801309385 title "Shapelets-Based Intrusion Detection for Protection Traffic Flooding Attacks" @default.
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• W2801309385 doi "https://doi.org/10.1007/978-3-319-91455-8_20" @default.
• W2801309385 hasPublicationYear "2018" @default.
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