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題名 利用 HTTP 封包採取非監督式深度學習演算法進行網路攻擊樣態分析
An Unsupervised Learning Approach for Cyber Attack Analysis with HTTP Payload Embedding
作者 陳唯哲
Chen, Wei-Zhe
貢獻者 蕭舜文
Hsiao, Shun-Wen
陳唯哲
Chen, Wei-Zhe
關鍵詞 語言模型
封包嵌入
NCCU
BERT
Packet embedding
MITRE ATT&CK
日期 2023
上傳時間 1-Sep-2023 14:55:52 (UTC+8)
摘要 網絡攻擊數量層出不窮，手段不斷創新。即使網絡安全專家進行分析，仍然相當耗時。因此，有必要開發一個利用人工智能進行大數據分析的自動化平台。與其觀察攻擊方式後進行事後驗證和防護，不如在攻擊發生前進行預測和分析。 如果我們能夠知道具有攻擊模式的事件（例如：偵查目標環境，或者竊取數據庫數據）正在發生，我們就可以主動防禦網絡攻擊。 我們觀察到，攻擊者會在不同的攻擊階段通過組合不同的技術來實施階段性策略（戰術），從而完成攻擊生命週期。 通過執行完整的攻擊過程來達到最終的攻擊目標。因此，找出不同階段的攻擊模式，就可以知道當前攻擊的進展情況，即可以在攻擊初期進行防禦。

在我們的方法中，我們構建了一個人工智能主動防禦系統，使用蜜罐來捕獲當前的攻擊，並分析其在特定事件期間（例如總統選舉日）的意圖和生命週期階段。自動生成攻擊模式的方法可以主動保護網絡服務免受網絡攻擊事件的影響，降低特定事件受網絡安全攻擊事件影響的風險。 我們開發神經算法將蜜罐數據包數據和蜜罐記錄文件轉換到高維空間，利用神經網絡對蜜罐收集的行為進行聚類和分析，自動預測其攻擊生命週期並自動生成其攻擊模式報告。對於收集到的蜜罐行為，本研究可以產生其生命週期各個階段的攻擊行為，網絡安全專家可以了解行為的發展情況並進行分析。這項研究成果可以減少網絡安全專家分析大量惡意攻擊日誌和數據包所花費的時間和成本，並生成高質量的網絡分析報告。
The number of cyber attacks emerges in an endless stream and the methods are constantly being innovated. Even if cybersecurity experts conduct analysis, it is still quite time-consuming. Therefore, it is necessary to develop an automated platform for big data analysis using artificial intelligence. Instead of doing post-event verification and protection after observing the attack method, it is better to predict and analyze the attack before it occurs. If we can know that an event with an attack pattern (for example: scouting the target environment, or stealing DB data) is happening, we can actively defend against network attacks. We have observed that attackers will implement staged strategies (tactics) by combining different techniques in different attack stages to complete the attack life cycle. The final attack goal is achieved by executing a complete attack process. Therefore, if you find out the attack pattern at different stages, you can know the current progress of the attack, that is, you can defend in the early stage of the attack.

In our approach, we build an artificial intelligence proactive defense system, use the Honeypot to trap the current attack, and analyze its intention and life cycle stage during a specific event period (e.g., the presidential election day). The method of automatically generating attack patterns can actively protect network services from cyber attack events and reduce the risk of specific events being affected by cybersecurity attack events. We develop neural algorithms to convert Honeypot packet data and Honeypot record files to high-dimensional space, use neural network to cluster and analyze the behaviors collected by Honeypot, automatically predict its attack life cycle and automatically generate its attack pattern report. For the collected Honeypot behaviors, this study can produce the attack behaviors in each stage of its life cycle, and cybersecurity experts can understand the development of the behaviors and conduct and analyze them. The results of this research can reduce the time and cost spent by cybersecurity experts in analyzing a large number of malicious attack logs and packets, and produce high-quality network analysis reports.
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Han, L., Sheng, Y., and Zeng, X. (2019). A packet-length-adjustable attention model based on bytes embedding using flow-wgan for smart cybersecurity. IEEE Access, 7:82913– 82926.
Hassan, M., Haque, M. E., Tozal, M. E., Raghavan, V., and Agrawal, R. (2021). Intrusion detection using payload embeddings. IEEE Access, 10:4015–4030.
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描述 碩士
國立政治大學
資訊管理學系
110356047
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110356047
資料類型 thesis
dc.contributor.advisor 蕭舜文zh_TW
dc.contributor.advisor Hsiao, Shun-Wenen_US
dc.contributor.author (Authors) 陳唯哲zh_TW
dc.contributor.author (Authors) Chen, Wei-Zheen_US
dc.creator (作者) 陳唯哲zh_TW
dc.creator (作者) Chen, Wei-Zheen_US
dc.date (日期) 2023en_US
dc.date.accessioned 1-Sep-2023 14:55:52 (UTC+8)-
dc.date.available 1-Sep-2023 14:55:52 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2023 14:55:52 (UTC+8)-
dc.identifier (Other Identifiers) G0110356047en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146898-
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) 110356047zh_TW
dc.description.abstract (摘要) 網絡攻擊數量層出不窮，手段不斷創新。即使網絡安全專家進行分析，仍然相當耗時。因此，有必要開發一個利用人工智能進行大數據分析的自動化平台。與其觀察攻擊方式後進行事後驗證和防護，不如在攻擊發生前進行預測和分析。 如果我們能夠知道具有攻擊模式的事件（例如：偵查目標環境，或者竊取數據庫數據）正在發生，我們就可以主動防禦網絡攻擊。 我們觀察到，攻擊者會在不同的攻擊階段通過組合不同的技術來實施階段性策略（戰術），從而完成攻擊生命週期。 通過執行完整的攻擊過程來達到最終的攻擊目標。因此，找出不同階段的攻擊模式，就可以知道當前攻擊的進展情況，即可以在攻擊初期進行防禦。

在我們的方法中，我們構建了一個人工智能主動防禦系統，使用蜜罐來捕獲當前的攻擊，並分析其在特定事件期間（例如總統選舉日）的意圖和生命週期階段。自動生成攻擊模式的方法可以主動保護網絡服務免受網絡攻擊事件的影響，降低特定事件受網絡安全攻擊事件影響的風險。 我們開發神經算法將蜜罐數據包數據和蜜罐記錄文件轉換到高維空間，利用神經網絡對蜜罐收集的行為進行聚類和分析，自動預測其攻擊生命週期並自動生成其攻擊模式報告。對於收集到的蜜罐行為，本研究可以產生其生命週期各個階段的攻擊行為，網絡安全專家可以了解行為的發展情況並進行分析。這項研究成果可以減少網絡安全專家分析大量惡意攻擊日誌和數據包所花費的時間和成本，並生成高質量的網絡分析報告。		zh_TW
dc.description.abstract (摘要) The number of cyber attacks emerges in an endless stream and the methods are constantly being innovated. Even if cybersecurity experts conduct analysis, it is still quite time-consuming. Therefore, it is necessary to develop an automated platform for big data analysis using artificial intelligence. Instead of doing post-event verification and protection after observing the attack method, it is better to predict and analyze the attack before it occurs. If we can know that an event with an attack pattern (for example: scouting the target environment, or stealing DB data) is happening, we can actively defend against network attacks. We have observed that attackers will implement staged strategies (tactics) by combining different techniques in different attack stages to complete the attack life cycle. The final attack goal is achieved by executing a complete attack process. Therefore, if you find out the attack pattern at different stages, you can know the current progress of the attack, that is, you can defend in the early stage of the attack.

In our approach, we build an artificial intelligence proactive defense system, use the Honeypot to trap the current attack, and analyze its intention and life cycle stage during a specific event period (e.g., the presidential election day). The method of automatically generating attack patterns can actively protect network services from cyber attack events and reduce the risk of specific events being affected by cybersecurity attack events. We develop neural algorithms to convert Honeypot packet data and Honeypot record files to high-dimensional space, use neural network to cluster and analyze the behaviors collected by Honeypot, automatically predict its attack life cycle and automatically generate its attack pattern report. For the collected Honeypot behaviors, this study can produce the attack behaviors in each stage of its life cycle, and cybersecurity experts can understand the development of the behaviors and conduct and analyze them. The results of this research can reduce the time and cost spent by cybersecurity experts in analyzing a large number of malicious attack logs and packets, and produce high-quality network analysis reports.		en_US
dc.description.tableofcontents Abstract i
Contents iv
List of Figures vi
List of Tables vii
1 Introduction 1
2 Background and Related work 5
2.1 Honeypot 5
2.2 Transformer 6
2.3 Cyber Attack Lifecycle 8
2.4 Clustering Algorithm 10
2.5 RelatedWork 11
3 Model Architecture 14
3.1 Overview 14
3.2 Payload Preprocessing 15
3.3 Data Labeling 17
3.4 Model Training and Attack Inferencing 17
3.4.1 Model Training Stage 18
3.4.2 Attack Inferencing Stage 21
3.5 Attack Pattern Discovery 22
4 Evaluation 28
4.1 Evaluation Setting 28
4.1.1 Data Collection 28
4.1.2 Implementation Details 31
4.1.3 Evaluation Metrics 31
4.1.4 Experimental Setup 32
4.2 Cyber Attack Tactic Inferencing 33
4.3 Attack Pattern Clustering & Analysis 34
5 Discussion and Future Work 41
6 Conclusion 43
Reference 44		zh_TW
dc.format.extent 4709406 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110356047en_US
dc.subject (關鍵詞) 語言模型zh_TW
dc.subject (關鍵詞) 封包嵌入zh_TW
dc.subject (關鍵詞) NCCUen_US
dc.subject (關鍵詞) BERTen_US
dc.subject (關鍵詞) Packet embeddingen_US
dc.subject (關鍵詞) MITRE ATT&CKen_US
dc.title (題名) 利用 HTTP 封包採取非監督式深度學習演算法進行網路攻擊樣態分析zh_TW
dc.title (題名) An Unsupervised Learning Approach for Cyber Attack Analysis with HTTP Payload Embeddingen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Abdullah, T. and Ahmet, A. (2022). Deep learning in sentiment analysis: Recent archi- tectures. ACM Computing Surveys, 55(8):1–37.
Bahaa, M., Aboulmagd, A., Adel, K., Fawzy, H., and Abdelbaki, N. (2020). nndpi: A novel deep packet inspection technique using word embedding, convolutional and re- current neural networks. In 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), pages 165–170. IEEE.
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the association for computational linguistics, 5:135–146.
Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):1–58.
Chowdhary, K. and Chowdhary, K. (2020). Natural language processing. Fundamentals of artificial intelligence, pages 603–649.
Combs, G. et al. (1998–2023). Wireshark: A network protocol analyzer. Accessed: 2023- 05-06.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training
of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Epp, N., Funk, R., Cappo, C., and Lorenzo-Paraguay, S. (2017). Anomaly-based web ap- plication firewall using http-specific features and one-class svm. In Workshop Regional de Segurança da Informação e de Sistemas Computacionais.
Gehring, J., Auli, M., Grangier, D., Yarats, D., and Dauphin, Y. N. (2017). Convolutional sequence to sequence learning. In International conference on machine learning, pages 1243–1252. PMLR.
GeoDB (2023). Geodb: The geospatial database. Accessed: 2023-05-06.
Goodman, E. L., Zimmerman, C., and Hudson, C. (2020). Packet2vec: Utilizing
word2vec for feature extraction in packet data. arXiv preprint arXiv:2004.14477.
Han, L., Sheng, Y., and Zeng, X. (2019). A packet-length-adjustable attention model based on bytes embedding using flow-wgan for smart cybersecurity. IEEE Access, 7:82913– 82926.
Hassan, M., Haque, M. E., Tozal, M. E., Raghavan, V., and Agrawal, R. (2021). Intrusion detection using payload embeddings. IEEE Access, 10:4015–4030.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
Hutchins, E. M., Cloppert, M. J., Amin, R. M., et al. (2011). Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains. Leading Issues in Information Warfare & Security Research, 1(1):80.
Hwang, R.-H., Peng, M.-C., Nguyen, V.-L., and Chang, Y.-L. (2019). An lstm-based deep
learning approach for classifying malicious traffic at the packet level. Applied Sciences, 9(16):3414.
Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3):264–323.
Jiao, X., Yin, Y., Shang, L., Jiang, X., Chen, X., Li, L., Wang, F., and Liu, Q. (2019). Tinybert: Distilling bert for natural language understanding. arXiv preprint arXiv:1909.10351.
Jin, X., Cui, B., Yang, J., and Cheng, Z. (2018). Payload-based web attack detection using deep neural network. In Advances on Broad-Band Wireless Computing, Communication and Applications: Proceedings of the 12th International Conference on Broad-Band Wireless Computing, Communication and Applications (BWCCA-2017), pages 482– 488. Springer.
Kriegel, H.-P., Kröger, P., Sander, J., and Zimek, A. (2011). Density-based clustering. Wiley interdisciplinary reviews: data mining and knowledge discovery, 1(3):231–240.
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