| dc.contributor.advisor | 蕭舜文 | zh_TW |
| dc.contributor.advisor | Hsiao, Shun-Wen | en_US |
| dc.contributor.author (Authors) | 陳羿丞 | zh_TW |
| dc.contributor.author (Authors) | Chen, Yi-Cheng | en_US |
| dc.creator (作者) | 陳羿丞 | zh_TW |
| dc.creator (作者) | Chen, Yi-Cheng | en_US |
| dc.date (日期) | 2024 | en_US |
| dc.date.accessioned | 4-Sep-2024 14:06:20 (UTC+8) | - |
| dc.date.available | 4-Sep-2024 14:06:20 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-Sep-2024 14:06:20 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0111356045 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/153163 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊管理學系 | zh_TW |
| dc.description (描述) | 111356045 | zh_TW |
| dc.description.abstract (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。 | zh_TW |
| dc.description.abstract (摘要) | 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. | en_US |
| dc.description.tableofcontents | 1. Introduction 1
2. Related Work 5
2.1 Language Representation Model 5
2.2 Anonymized System Logs 7
2.3 LM Log Analysis 8
2.3.1 Log Parsers 8
2.3.2 Anomaly Detection 8
2.3.3 Misuse Detection 9
2.3.4 Discussion 10
3. Methodology 11
3.1 Overview 11
3.2 Anonymization Preprocessing 13
3.3 Pre-Training Tasks 15
3.3.1 Masked Language Modeling 15
3.3.2 Shuffled Token Detection 16
3.4 Two-Layer Language Model 17
4. Evaluation 22
4.1 Data Set 22
4.2 Implementation 23
4.3 Experiments 24
4.3.1 Single-layer versus two-layer 24
4.3.2 Ablation Test 26
4.3.3 Model Evaluation 29
4.3.4 Denoising Mechanism 30
5. Conclusion 32
Reference 33 | zh_TW |
| dc.format.extent | 1943832 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0111356045 | en_US |
| dc.subject (關鍵詞) | 系統日誌 | zh_TW |
| dc.subject (關鍵詞) | 語言模型 | zh_TW |
| dc.subject (關鍵詞) | 自監督學習 | zh_TW |
| dc.subject (關鍵詞) | 單一分類 | zh_TW |
| dc.subject (關鍵詞) | 去識別化 | zh_TW |
| dc.subject (關鍵詞) | System logs | en_US |
| dc.subject (關鍵詞) | Language models | en_US |
| dc.subject (關鍵詞) | Self-supervised | en_US |
| dc.subject (關鍵詞) | One-class classification | en_US |
| dc.subject (關鍵詞) | Anonymization | en_US |
| dc.title (題名) | 使用兩層語言模型的自監督日誌異常檢測 | zh_TW |
| dc.title (題名) | Self-Supervised Log Anomaly Detection Using Two-Layer Language Model | en_US |
| dc.type (資料類型) | thesis | en_US |
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