Anomaly Detection Using an MMPP-based GLRT

Detection of anomalous network traffic is accomplished using a generalized likelihood ratio test (GLRT) applied to traffic arrival times. The network traffic arrival times are modelled using a Markov modulated Poisson process (MMPP). The GLRT is implemented using an estimate of the MMPP parameter obtained from training data that is not anomalous. MMPP parameter estimation is accomplished using Rydén's expectation-maximization (EM) approach. Using data from the 1999DARPA intrusion detection evaluation, the performance of a GLRT using an MMPP, a Poisson process, and a mixture of exponentials is compared. The MMPP-based GLRT has the best performance and the largest computational requirements.

並列關鍵字

Anomaly detection ； generalized likelihood ratio test ； markov-modulated Poisson process

被引用紀錄

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陳怡璇（2011）。成核控制對於尼龍6多孔薄膜於浸漬沉澱相轉換法之研究〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2011.00885

Tsai, T. N. (2014). 使用定位同步技術之非同步電路自動化流程設計 [master's thesis, National Chiao Tung University]. Airiti Library. https://doi.org/10.6842/NCTU.2014.00610

蕭雅純（2014）。定位不明的照護者-論專科護理師之執業範圍與資格標準〔碩士論文，國立交通大學〕。華藝線上圖書館。https://doi.org/10.6842/NCTU.2014.00348

洪筱倩（2014）。跨運具轉乘縫隙之指標〔碩士論文，國立交通大學〕。華藝線上圖書館。https://doi.org/10.6842/NCTU.2014.00326

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Anomaly Detection Using an MMPP-based GLRT

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