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    題名: 使用兩層語言模型的自監督日誌異常檢測
    Self-Supervised Log Anomaly Detection Using Two-Layer Language Model
    作者: 陳羿丞
    Chen, Yi-Cheng
    貢獻者: 蕭舜文
    Hsiao, Shun-Wen
    陳羿丞
    Chen, Yi-Cheng
    關鍵詞: 系統日誌
    語言模型
    自監督學習
    單一分類
    去識別化
    System logs
    Language models
    Self-supervised
    One-class classification
    Anonymization
    日期: 2024
    上傳時間: 2024-09-04 14:06:20 (UTC+8)
    摘要: 隨著系統日益複雜以及潛在攻擊者的利用，機器生成數據（如安全日誌和監控信息）的海量且不斷增長，迫切需要及早檢測異常。語言模型在日誌異常檢測中面臨的主要挑戰包括：檢測不同粒度的異常、處理解析錯誤和日誌解析器導致的語義信息丟失、缺乏標註數據需要無監督異常檢測方法，以及在將分析外包時需要去噪和匿名機制以保護隱私。

    為了解決這些挑戰，我們提出了一種自監督的兩層語言模型，利用BERT和Transformer編碼器來考慮不同層次的異常。我們的匿名化預處理技術消除了對日誌解析器的依賴並保護隱私。同時，我們將兩層語言模型與去噪機制和單類分類結合起來。

    在多個數據集上的實驗結果證明了我們方法的有效性，在檢測異常方面達到了高精度和高召回率。我們提出的方法為日誌異常檢測提供了一個強有力的解決方案。
    The immense and ever-growing volume of machine-generated data, including security logs and monitoring information, necessitates early anomaly detection due to increasing system complexity and potential exploitation by attackers.
    The primary challenges for language models in log anomaly detection include detecting different granularity of anomalies, handling parsing errors and loss of semantic information from log parsers, lack of labeled data requiring unsupervised anomaly detection approaches, the need for the denoising mechanism, and anonymization for privacy protection if outsourcing the analysis.
    To address these challenges, we propose the self-supervised two-layer language model that utilizes BERT and the transformer encoder to consider anomalies at different levels. The anonymization preprocessing technique eliminates reliance on log parsers and protects privacy. We also integrate the two-layer language model with a denoising mechanism and one-class classification.
    Experimental results on multiple datasets demonstrate the effectiveness of our approach, achieving high precision and recall rates in detecting anomalies.
    The proposed method offers a robust solution for log anomaly detection.
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    描述: 碩士
    國立政治大學
    資訊管理學系
    111356045
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0111356045
    資料類型: thesis
    顯示於類別:[資訊管理學系] 學位論文

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