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    題名: 於系統日誌使用語言模型的異常分析
    Anomaly Detection on System Log with Language Modeling
    作者: 曾志中
    Tseng, Chih-Chung
    貢獻者: 蕭舜文
    Hsiao, Shun-Wen
    曾志中
    Tseng, Chih-Chung
    關鍵詞: 系統日誌分析
    異常分析
    深度學習
    log data analysis
    anomaly detection
    deep learning
    日期: 2022
    上傳時間: 2022-09-02 14:48:21 (UTC+8)
    摘要: 為管理系統服務品質，系統日誌廣泛地存在於應用軟體之中，而其中的異常行為與錯誤可能導致軟體漏洞的產生，並使服務暴露於危險之中。因此，系統維運人員通常採用異常偵測以及時發現不尋常的事件發生。隨著自然語言處理在近年的發展，分析系統日誌的研究開始採納語言表徵模型，讓預測模型也能考慮系統日誌背後的語意。這樣的方法使預測模型更能應付不斷變化的日誌格式。我們提出一個具有重建閘且基於BERT的單類別預測模型，於不同層級下學習系統日誌的正常行為。我們的方法結合了異常分析的訓練目標與語意的表徵，且透過組合的惡意分數，來反映連續事件中細微的異常。我們以兩個截然不同的資料集來評估我們的方法，而實驗結果展現出此模型對於複雜的系統日誌具有優秀的適應能力，並透過序列分析中的統計數據來解釋我們的成果。
    System log is generally existing in software applications to help operators manage their services. Misbehavior and bugs in a system can cause vulnerabilities and put services in danger. Therefore, anomaly detection is adopted to aid operators to discover anomalous events in system log. With the development of deep learning models in Natural Language Processing (NLP), recent researches utilize language representation models to take semantics behind the log into consideration. The approach strengthens the adaptability of an anomaly detection model to log events with changing formats. We propose the Bert-based One-class classification with an explicit Reconstruction Gate (BORG) to recognize the benign session behavior of system log in different levels. Our method integrates the anomaly detection objective with language representation, and comprise a composite malicious score in the detection phase to reflect the abnormality in trivial events. We evaluate our concept under two log data sets with contrasting statistic properties. The result shows the robustness of our method to challenging log data. The experiments and analysis are also presented to explain our outcomes.
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    描述: 碩士
    國立政治大學
    資訊管理學系
    109356019
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0109356019
    資料類型: thesis
    DOI: 10.6814/NCCU202201200
    顯示於類別:[資訊管理學系] 學位論文

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