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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/110477


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    题名: Slow-Paced Persistent Network Attacks Analysis and Detection Using Spectrum Analysis
    作者: 陳力銘;蕭舜文;陳孟彰;廖婉君
    贡献者: 資管系
    日期: 2016-12
    上传时间: 2017-06-23 17:28:35 (UTC+8)
    摘要: A slow-paced persistent attack, such as slow worm or bot, can bewilder the detection system by slowing down their attack. Detecting such attacks based on traditional anomaly detection techniques may yield high false alarm rates. In this paper, we frame our problem as detecting slow-paced persistent attacks from a time series obtained from network trace. We focus on time series spectrum analysis to identify peculiar spectral patterns that may represent the occurrence of a persistent activity in the time domain. We propose a method to adaptively detect slow-paced persistent attacks in a time series and evaluate the proposed method by conducting experiments using both synthesized traffic and real-world traffic. The results show that the proposed method is capable of detecting slow-paced persistent attacks even in a noisy environment mixed with legitimate traffic.
    關聯: IEEE Systems Journal, Vol.10, No.4, pp.1326-1337
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1109/JSYST.2014.2348567
    DOI: 10.1109/JSYST.2014.2348567
    显示于类别:[資訊管理學系] 期刊論文

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