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題名 應用Transformer架構之Cloud-Native VoIP系統異常偵測與輔助處理研究
A Study on Anomaly Detection and Assisted Handling in Cloud-Native VoIP Systems Using Transformer Architecture
作者 趙璿
Chao, Hsuan
貢獻者 張宏慶
Jang, Hung-Chin
趙璿
Chao, Hsuan
關鍵詞 雲端原生網路功能
基於IP的語音傳輸系統
異常偵測
Transformer
大型語言模型
Cloud-Native Network Function
Voice over IP
Anomaly Detection
Transformer
Large Language Model
日期 2025
上傳時間 1-Sep-2025 16:19:34 (UTC+8)
參考文獻 [1] S. Sekigawa, C. Sasaki and A. Tagami, "Toward a Cloud-Native Telecom Infrastructure: Analysis and Evaluations of Kubernetes Networking," 2022 IEEE Globecom Workshops (GC Wkshps), Rio de Janeiro, Brazil, 2022, pp. 838-843, doi: 10.1109/GCWkshps56602.2022.10008579. [2] C. Liu, Z. Cai, B. Wang, Z. Tang and J. Liu, "A protocol-independent container network observability analysis system based on eBPF," 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), Hong Kong, 2020, pp. 697-702, doi: 10.1109/ICPADS51040.2020.00099. [3] C. Wen-Lin, "Intelligent Analysis for Abnormal Signaling Detection on NGN Voice Call," 2022 7th International Conference on Information and Network Technologies (ICINT), Okinawa, Japan, 2022, pp. 10-15, doi: 10.1109/ICINT55083.2022.00009. [4] D. Gedia and L. Perigo, "Decision-Tree Placement Algorithm for Containerized VoIP VNFs: A Network Management Approach," 2022 25th Conference on Innovation in Clouds, Internet and Networks (ICIN), Paris, France, 2022, pp. 1-5, doi: 10.1109/ICIN53892.2022.9758119. [5] W. -C. Chung, H. -T. Liang and Y. -H. Wang, "Performance Impacts of Scaling Policies for virtual IP Multimedia Subsystem on the Cloud," 2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Phoenix, AZ, USA, 2022, pp. 13-18, doi: 10.1109/NFV-SDN56302.2022.9974953. [6] R. Chowdhury, C. Talhi, H. Ould-Slimane and A. Mourad, "Proactive and Intelligent Monitoring and Orchestration of Cloud-Native IP Multimedia Subsystem," in IEEE Open Journal of the Communications Society, vol. 5, pp. 139-155, 2024, doi: 10.1109/OJCOMS.2023.3341002. keywords: {Cloud computing;Monitoring;Microservice [7] F. Cecchinato, L. Vangelista, G. Biondo and M. Franchin, "Anomaly detection using LSTM neural networks: an application to VoIP traffic," 2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), Shanghai, China, 2021, pp. 1-7, doi: 10.1109/RASSE53195.2021.9686840. [8] A. Diamanti, J. M. S. Vílchez and S. Secci, "An AI-Empowered Framework for Cross-Layer Softwarized Infrastructure State Assessment," in IEEE Transactions on Network and Service Management, vol. 19, no. 4, pp. 4434-4448, Dec. 2022, doi: 10.1109/TNSM.2022.3161872 [9] H. Zhang, Y. Xia, T. Yan and G. Liu, "Unsupervised Anomaly Detection in Multivariate Time Series through Transformer-based Variational Autoencoder," 2021 33rd Chinese Control and Decision Conference (CCDC), Kunming, China, 2021, pp. 281-286, doi: 10.1109/CCDC52312.2021.9601669. [10] J. Kang, M. Kim, J. Park and S. Park, "Time-Series to Image-Transformed Adversarial Autoencoder for Anomaly Detection," in IEEE Access, vol. 12, pp. 119671-119684, 2024, doi: 10.1109/ACCESS.2024.3450709. [11] Y. Fang, J. Xie, Y. Zhao, L. Chen, Y. Gao and K. Zheng, "Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection," 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 2024, pp. 1228-1241, doi: 10.1109/ICDE60146.2024.00099. [12] K. Aykurt, A. Blenk and W. Kellerer, "NetLLMBench: A Benchmark Framework for Large Language Models in Network Configuration Tasks," 2024 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Natal, Brazil, 2024, pp. 1-6, doi: 10.1109/NFV-SDN61811.2024.10807499. [13] B. Bokkena, "Optimizing Cloud Infrastructure Management Using Large Language Models: A DevOps Perspective," 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2024, pp. 1401-1406, doi: 10.1109/ICSSAS64001.2024.10760725. [14] Bahaa, A.; Shehata, M.; Gasser, S.M.; El-Mahallawy, M.S. Call Failure Prediction in IP Multimedia Subsystem (IMS) Networks. Appl. Sci. 2022, 12, 8378. https://doi.org/10.3390/app12168378 [15] M. D. Mauro, G. Galatro, F. Postiglione and M. Tambasco, "Performability of Network Service Chains: Stochastic Modeling and Assessment of Softwarized IP Multimedia Subsystem," in IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 5, pp. 3071-3086, 1 Sept.-Oct. 2022, doi: 10.1109/TDSC.2021.3082626. [16] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. arXiv. https://arxiv.org/abs/1706.03762 [17] E. Schooler, J. Rosenberg, H. Schulzrinne, A. Johnston, G. Camarillo, J. Peterson, R. Sparks, and M. J. Handley, “RFC 3261: SIP: Session Initiation Protocol,” IETF, 2002. [Online]. Available: https://datatracker.ietf.org/doc/html/rfc3261 [18] Prometheus, "Configuration," Prometheus Documentation. [Online]. Available: https://prometheus.io/docs/prometheus/latest/configuration/configuration/ [19] Google, “Prompt engineering techniques,” Google Developers, [Online]. Available: https://developers.google.com/machine-learning/resources/prompt-eng?hl=zh-tw [20] Kamailio, “Kamailio v5.5.x Module Documentation,” [Online]. Available: https://kamailio.org/docs/modules/5.5.x/ [21] W. Attaoui, E. Sabir, H. Elbiaze and M. Guizani, "VNF and CNF Placement in 5G: Recent Advances and Future Trends," in IEEE Transactions on Network and Service Management, vol. 20, no. 4, pp. 4698-4733, Dec. 2023, doi: 10.1109/TNSM.2023.3264005. [22] A. I. Kouachi and I. E. Mokrane, "Leveraging Large Language Models for Base Station in Telecommunications," 2024 1st International Conference on Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech), Oum El Bouaghi, Algeria, 2024, pp. 1-8, doi: 10.1109/ECTE-Tech62477.2024.10851026. [23] A. Dubey, C. P. Singh and D. Nadig, "Leveraging Large Language Models for Intent-Based Generation of Cloud-Native Configurations," 2024 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Guwahati, India, 2024, pp. 1-6, doi: 10.1109/ANTS63515.2024.10898244. [24] V. -H. Le and H. Zhang, "Log Parsing: How Far Can ChatGPT Go?," 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), Luxembourg, Luxembourg, 2023, pp. 1699-1704, doi: 10.1109/ASE56229.2023.00206. [25] K. Panchal et al., "A Study on the Use of Runtime Files in Handling Crash Reports in a Large Telecom Company," 2022 IEEE Future Networks World Forum (FNWF), Montreal, QC, Canada, 2022, pp. 98-103, doi: 10.1109/FNWF55208.2022.00026. [26] Y. Kim and P. Thulasiraman, "Anomaly Detection in 5G Networks Using Transformer-Based Autoencoder," 2024 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), Taichung, Taiwan, 2024, pp. 1-8, doi: 10.1109/RASSE64357.2024.10773774. [27] K. Zhang, G. Lu and Y. Li, "Trans-DAE: Transformer-based Double Autoencoder for Anomaly Detection on Attributed Networks," 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Harbin, China, 2023, pp. 1-9, doi: 10.1109/ICNC-FSKD59587.2023.10280911. [28] A. Hilt, J. Zemán and G. Járó, "Dimensioning Challenges of Telecommunication Network Elements Migrating Onto the Cloud (Invited Paper)," 2024 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom), Graz, Austria, 2024, pp. 1-10, doi: 10.1109/CoBCom62281.2024.10631212. [29] X. Chen, S. Wang, S. -a. Wang, B. Ding, R. Yan and X. Chen, "Algorithm Unrolling Network With Learnable Sparse Regularization for Interpretable Mechanical Anomaly Detection," in IEEE Transactions on Industrial Informatics, vol. 21, no. 5, pp. 3786-3795, May 2025, doi: 10.1109/TII.2025.3528557. [30] Z. Yang, Y. Jin, J. Liu, X. Xu, Y. Zhang and S. Ji, "Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models," 2025 IEEE 7th International Conference on Communications, Information System and Computer Engineering (CISCE), Guangzhou, China, 2025, pp. 1029-1032, doi: 10.1109/CISCE65916.2025.11065413. [31] Alvares C, Dinesh D, Alvi S, et al. Dataset of attacks on a live enterprise VoIP network for machine learning based intrusion detection and prevention systems[J]. Computer Networks, 2021 : 108283.
描述 碩士
國立政治大學
資訊科學系碩士在職專班
112971011
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112971011
資料類型 thesis
dc.contributor.advisor 張宏慶zh_TW
dc.contributor.advisor Jang, Hung-Chinen_US
dc.contributor.author (Authors) 趙璿zh_TW
dc.contributor.author (Authors) Chao, Hsuanen_US
dc.creator (作者) 趙璿zh_TW
dc.creator (作者) Chao, Hsuanen_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Sep-2025 16:19:34 (UTC+8)-
dc.date.available 1-Sep-2025 16:19:34 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2025 16:19:34 (UTC+8)-
dc.identifier (Other Identifiers) G0112971011en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159297-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系碩士在職專班zh_TW
dc.description (描述) 112971011zh_TW
dc.description.tableofcontents 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目標 3 1.4 論文架構 3 第二章 相關研究 4 2.1 Cloud-Native VoIP架構與維運挑戰 4 2.2 VoIP系統異常偵測技術與應用 7 2.3 Transformer架構於異常偵測之應用 9 2.4 大型語言模型於異常處理建議之應用潛力 12 第三章 研究方法 15 3.1 問題分析 15 3.2 系統架構 16 3.3 VoIP與流量模擬平台 17 3.4 異常偵測模組 18 3.5 語言模型模組 22 3.6 系統流程 24 第四章 模擬實驗與結果分析 27 4.1 實驗模擬環境架構 27 4.2 測試方法流程 28 4.3 測試資料收集 29 4.4 異常情境設計 32 4.5 模型訓練實作 34 4.6 異常偵測分析 37 4.7 語言模型分析 44 4.8 實驗結果 48 第五章 結論與未來研究 49 5.1 結論 49 5.2 未來工作及研究限制 50 參考文獻 51zh_TW
dc.format.extent 2803426 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112971011en_US
dc.subject (關鍵詞) 雲端原生網路功能zh_TW
dc.subject (關鍵詞) 基於IP的語音傳輸系統zh_TW
dc.subject (關鍵詞) 異常偵測zh_TW
dc.subject (關鍵詞) Transformerzh_TW
dc.subject (關鍵詞) 大型語言模型zh_TW
dc.subject (關鍵詞) Cloud-Native Network Functionen_US
dc.subject (關鍵詞) Voice over IPen_US
dc.subject (關鍵詞) Anomaly Detectionen_US
dc.subject (關鍵詞) Transformeren_US
dc.subject (關鍵詞) Large Language Modelen_US
dc.title (題名) 應用Transformer架構之Cloud-Native VoIP系統異常偵測與輔助處理研究zh_TW
dc.title (題名) A Study on Anomaly Detection and Assisted Handling in Cloud-Native VoIP Systems Using Transformer Architectureen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] S. Sekigawa, C. Sasaki and A. Tagami, "Toward a Cloud-Native Telecom Infrastructure: Analysis and Evaluations of Kubernetes Networking," 2022 IEEE Globecom Workshops (GC Wkshps), Rio de Janeiro, Brazil, 2022, pp. 838-843, doi: 10.1109/GCWkshps56602.2022.10008579. [2] C. Liu, Z. Cai, B. Wang, Z. Tang and J. Liu, "A protocol-independent container network observability analysis system based on eBPF," 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), Hong Kong, 2020, pp. 697-702, doi: 10.1109/ICPADS51040.2020.00099. [3] C. Wen-Lin, "Intelligent Analysis for Abnormal Signaling Detection on NGN Voice Call," 2022 7th International Conference on Information and Network Technologies (ICINT), Okinawa, Japan, 2022, pp. 10-15, doi: 10.1109/ICINT55083.2022.00009. [4] D. Gedia and L. Perigo, "Decision-Tree Placement Algorithm for Containerized VoIP VNFs: A Network Management Approach," 2022 25th Conference on Innovation in Clouds, Internet and Networks (ICIN), Paris, France, 2022, pp. 1-5, doi: 10.1109/ICIN53892.2022.9758119. [5] W. -C. Chung, H. -T. Liang and Y. -H. Wang, "Performance Impacts of Scaling Policies for virtual IP Multimedia Subsystem on the Cloud," 2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Phoenix, AZ, USA, 2022, pp. 13-18, doi: 10.1109/NFV-SDN56302.2022.9974953. [6] R. Chowdhury, C. Talhi, H. Ould-Slimane and A. Mourad, "Proactive and Intelligent Monitoring and Orchestration of Cloud-Native IP Multimedia Subsystem," in IEEE Open Journal of the Communications Society, vol. 5, pp. 139-155, 2024, doi: 10.1109/OJCOMS.2023.3341002. keywords: {Cloud computing;Monitoring;Microservice [7] F. Cecchinato, L. Vangelista, G. Biondo and M. Franchin, "Anomaly detection using LSTM neural networks: an application to VoIP traffic," 2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), Shanghai, China, 2021, pp. 1-7, doi: 10.1109/RASSE53195.2021.9686840. [8] A. Diamanti, J. M. S. Vílchez and S. Secci, "An AI-Empowered Framework for Cross-Layer Softwarized Infrastructure State Assessment," in IEEE Transactions on Network and Service Management, vol. 19, no. 4, pp. 4434-4448, Dec. 2022, doi: 10.1109/TNSM.2022.3161872 [9] H. Zhang, Y. Xia, T. Yan and G. Liu, "Unsupervised Anomaly Detection in Multivariate Time Series through Transformer-based Variational Autoencoder," 2021 33rd Chinese Control and Decision Conference (CCDC), Kunming, China, 2021, pp. 281-286, doi: 10.1109/CCDC52312.2021.9601669. [10] J. Kang, M. Kim, J. Park and S. Park, "Time-Series to Image-Transformed Adversarial Autoencoder for Anomaly Detection," in IEEE Access, vol. 12, pp. 119671-119684, 2024, doi: 10.1109/ACCESS.2024.3450709. [11] Y. Fang, J. Xie, Y. Zhao, L. Chen, Y. Gao and K. Zheng, "Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection," 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 2024, pp. 1228-1241, doi: 10.1109/ICDE60146.2024.00099. [12] K. Aykurt, A. Blenk and W. Kellerer, "NetLLMBench: A Benchmark Framework for Large Language Models in Network Configuration Tasks," 2024 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Natal, Brazil, 2024, pp. 1-6, doi: 10.1109/NFV-SDN61811.2024.10807499. [13] B. Bokkena, "Optimizing Cloud Infrastructure Management Using Large Language Models: A DevOps Perspective," 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2024, pp. 1401-1406, doi: 10.1109/ICSSAS64001.2024.10760725. [14] Bahaa, A.; Shehata, M.; Gasser, S.M.; El-Mahallawy, M.S. Call Failure Prediction in IP Multimedia Subsystem (IMS) Networks. Appl. Sci. 2022, 12, 8378. https://doi.org/10.3390/app12168378 [15] M. D. Mauro, G. Galatro, F. Postiglione and M. Tambasco, "Performability of Network Service Chains: Stochastic Modeling and Assessment of Softwarized IP Multimedia Subsystem," in IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 5, pp. 3071-3086, 1 Sept.-Oct. 2022, doi: 10.1109/TDSC.2021.3082626. [16] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. arXiv. https://arxiv.org/abs/1706.03762 [17] E. Schooler, J. Rosenberg, H. Schulzrinne, A. Johnston, G. Camarillo, J. Peterson, R. Sparks, and M. J. Handley, “RFC 3261: SIP: Session Initiation Protocol,” IETF, 2002. [Online]. Available: https://datatracker.ietf.org/doc/html/rfc3261 [18] Prometheus, "Configuration," Prometheus Documentation. [Online]. Available: https://prometheus.io/docs/prometheus/latest/configuration/configuration/ [19] Google, “Prompt engineering techniques,” Google Developers, [Online]. Available: https://developers.google.com/machine-learning/resources/prompt-eng?hl=zh-tw [20] Kamailio, “Kamailio v5.5.x Module Documentation,” [Online]. Available: https://kamailio.org/docs/modules/5.5.x/ [21] W. Attaoui, E. Sabir, H. Elbiaze and M. Guizani, "VNF and CNF Placement in 5G: Recent Advances and Future Trends," in IEEE Transactions on Network and Service Management, vol. 20, no. 4, pp. 4698-4733, Dec. 2023, doi: 10.1109/TNSM.2023.3264005. [22] A. I. Kouachi and I. E. Mokrane, "Leveraging Large Language Models for Base Station in Telecommunications," 2024 1st International Conference on Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech), Oum El Bouaghi, Algeria, 2024, pp. 1-8, doi: 10.1109/ECTE-Tech62477.2024.10851026. [23] A. Dubey, C. P. Singh and D. Nadig, "Leveraging Large Language Models for Intent-Based Generation of Cloud-Native Configurations," 2024 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Guwahati, India, 2024, pp. 1-6, doi: 10.1109/ANTS63515.2024.10898244. [24] V. -H. Le and H. Zhang, "Log Parsing: How Far Can ChatGPT Go?," 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), Luxembourg, Luxembourg, 2023, pp. 1699-1704, doi: 10.1109/ASE56229.2023.00206. [25] K. Panchal et al., "A Study on the Use of Runtime Files in Handling Crash Reports in a Large Telecom Company," 2022 IEEE Future Networks World Forum (FNWF), Montreal, QC, Canada, 2022, pp. 98-103, doi: 10.1109/FNWF55208.2022.00026. [26] Y. Kim and P. Thulasiraman, "Anomaly Detection in 5G Networks Using Transformer-Based Autoencoder," 2024 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), Taichung, Taiwan, 2024, pp. 1-8, doi: 10.1109/RASSE64357.2024.10773774. [27] K. Zhang, G. Lu and Y. Li, "Trans-DAE: Transformer-based Double Autoencoder for Anomaly Detection on Attributed Networks," 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Harbin, China, 2023, pp. 1-9, doi: 10.1109/ICNC-FSKD59587.2023.10280911. [28] A. Hilt, J. Zemán and G. Járó, "Dimensioning Challenges of Telecommunication Network Elements Migrating Onto the Cloud (Invited Paper)," 2024 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom), Graz, Austria, 2024, pp. 1-10, doi: 10.1109/CoBCom62281.2024.10631212. [29] X. Chen, S. Wang, S. -a. Wang, B. Ding, R. Yan and X. Chen, "Algorithm Unrolling Network With Learnable Sparse Regularization for Interpretable Mechanical Anomaly Detection," in IEEE Transactions on Industrial Informatics, vol. 21, no. 5, pp. 3786-3795, May 2025, doi: 10.1109/TII.2025.3528557. [30] Z. Yang, Y. Jin, J. Liu, X. Xu, Y. Zhang and S. Ji, "Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models," 2025 IEEE 7th International Conference on Communications, Information System and Computer Engineering (CISCE), Guangzhou, China, 2025, pp. 1029-1032, doi: 10.1109/CISCE65916.2025.11065413. [31] Alvares C, Dinesh D, Alvi S, et al. Dataset of attacks on a live enterprise VoIP network for machine learning based intrusion detection and prevention systems[J]. Computer Networks, 2021 : 108283.zh_TW