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題名 物聯網異常診斷平台：以環境物聯網為例
Anomaly Diagnostic Platform for IoT: Using Environmental IoT as an Example作者 黃彥魁
Huang, Yen-Kuei貢獻者 沈錳坤
黃彥魁
Huang, Yen-Kuei關鍵詞 物聯網
異常診斷
樣式探勘
Internet of Things(IoT)
Anomaly diagnosis
Pattern mining日期 2022 上傳時間 5-Oct-2022 09:13:29 (UTC+8) 摘要 隨著網路的普及和感測器成本的降低，物聯網也逐漸興起。透過物聯網，可以監測不合預期的異常症狀。現有物聯網異常相關技術的研究，主要著重在異常事件的偵測，較少異常事件成因診斷的研究。導致異常事件的成因可能是觀測環境的異常或儀器設備本身的異常。針對異常成因的診斷，現有研究都集中在網路攻擊的異常事件。本論文以環境物聯網為例，研究物聯網異常事件診斷的方法。我們歸納整理空汙環境物聯網的異常症狀、時空線索與成因，提出物聯網異常事件診斷的方法與流程。根據我們所提出的診斷流程，設計實作一個異常事件診斷系統，提供使用者透過聚焦、歸納、對比，逐步地由眾多可能的線索中，探索出最可能導致異常事件的成因。本論文並以真實案例，實證我們所提出的系統可以協助使用者方便有效地找出異常成因。
With the spread of the internet and the cost reduction of sensors, the Internet of Things (IoT) became more popular. People can monitor and detect unexpected anomaly symptoms using IoT. Most of the existing research focuses on anomaly event detection while little research has been paid to the anomaly event diagnosis. Anomaly event may come from the deviation in environment or malfunction of devices themselves. Most current work on anomaly event diagnosis aim at the malicious attacks in IoT network.This thesis investigated the method of anomaly event diagnosis using environmental IoT as an example. We organized anomaly symptoms, temporal clues, spatial clues, and the root causes of anomaly events in environmental IoT for air pollution. This thesis also proposed the method and the procedure to diagnose anomaly events. According to the proposed procedure, this thesis designed and implemented an anomaly diagnosis system. The system provides the ability to focus, organize and compare the clues for anomaly diagnosis. It helps users to rule out unlikely root causes and explore possible root causes that triggered anomaly events. The proposed approach is demonstrated by real cases to show that the system could assist users to explore the root causes of anomaly events conveniently and effectively.參考文獻 [1]C. Tsai, C. Lai, M. Chiang, and L. T. Yang, “Data Mining for Internet of Things: A Survey,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 77-97, 2014.[2]F. Chen, P. Deng, J. Wan, D. Zhang, A. V. Vasilakos, and X. Rong, “Data Mining for the Internet of Things: Literature Review and Challenges,” International Journal of Distributed Sensor Networks, vol. 11, no. 8, 2015.[3]G. Atluri, A. Karpatne, and V. Kumar, “Spatio-Temporal Data Mining: A Survey of Problems and Methods,” ACM Computing Surveys, vol. 51, no. 4, pp. 1-41, 2018.[4]O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. H. D. N. Hindia, “An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges,” IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3758-3773, 2018.[5]A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of Things for Smart Cities,” IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22-32, 2014.[6]R. Jurdak, X. R. Wang, O. Obst, and P. Valencia, "Wireless Sensor Network Anomalies: Diagnosis and Detection Strategies," Intelligence-Based Systems Engineering, Intelligent Systems Reference Library, pp. 309-325, 2011.[7]V. Chandola, A. Banerjee, and V. Kumar, “Anomaly Detection: A Survey,” ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, 2009.[8]V. P. Illiano and E. C. Lupu, “Detecting Malicious Data Injections in Wireless Sensor Networks: A Survey,” ACM Computing Surveys, vol. 48, no. 2, pp. 1-33, 2015.[9]A. A. Cook, G. Mısırlı, and Z. Fan, “Anomaly Detection for IoT Time-Series Data: A Survey,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6481-6494, 2020.[10]L. Erhan, M. Ndubuaku, M. Di Mauro, W. Song, M. Chen, G. Fortino, O. Bagdasar, and A. Liotta, “Smart Anomaly Detection in Sensor Systems: A Multi-perspective Review,” Information Fusion, vol. 67, pp. 64-79, 2021.[11]M. Fahim and A. Sillitti, “Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review,” IEEE Access, vol. 7, pp. 81664-81681, 2019.[12]M. Hasan, M. M. Islam, M. I. I. Zarif, and M. M. A. Hashem, “Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches,” Internet of Things, vol. 7, 2019.[13]I. Alrashdi, A. Alqazzaz, E. Aloufi, R. Alharthi, M. Zohdy, and H. Ming, “AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning,” in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, pp. 305-310, 2019.[14]Z. Chen, L. Tian, and C. Lin, "A Method for Detection of Anomaly Node in IOT," Algorithms and Architectures for Parallel Processing, Lecture Notes in Computer Science, pp. 777-784, 2015.[15]Z. Deng, D. Weng, J. Chen, R. Liu, Z. Wang, J. Bao, Y. Zheng, and Y. Wu, “AirVis: Visual Analytics of Air Pollution Propagation,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 800-810, 2020.[16]L. Chen, Y. Ho, H. Hsieh, S. Huang, H. Lee, and S. Mahajan, “ADF: An Anomaly Detection Framework for Large-Scale PM2.5 Sensing Systems,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 559-570, 2018.[17]A. Rodrigues, T. Camilo, J. S. Silva, and F. Boavida, “Diagnostic Tools for Wireless Sensor Networks: A Comparative Survey,” Journal of Network and Systems Management, vol. 21, no. 3, pp. 408-452, 2013.[18]S. Chou, H. Yen, and A. Pang, “A REM-Enabled Diagnostic Framework in Cellular-Based IoT Networks,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5273-5284, 2019.[19]D. Rodenas-Herráiz, P. R. A. Fidler, T. Feng, X. Xu, S. Nawaz, and K. Soga, “A Handheld Diagnostic System for 6LoWPAN Networks,” in 2017 13th Annual Conference on Wireless On-demand Network Systems and Services, pp. 104-111, 2017.[20]A. Mahapatro and P. M. Khilar, “Fault Diagnosis in Wireless Sensor Networks: A Survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2000-2026, 2013.[21]D. Li, Y. Wang, J. Wang, C. Wang, and Y. Duan, “Recent Advances in Sensor Fault Diagnosis: A Review,” Sensors and Actuators A: Physical, vol. 309, 2020.[22]Z. Zhang, A. Mehmood, L. Shu, Z. Huo, Y. Zhang, and M. Mukherjee, “A Survey on Fault Diagnosis in Wireless Sensor Networks,” IEEE Access, vol. 6, pp. 11349-11364, 2018.[23]C. Wang, H. T. Vo, and P. Ni, “An IoT Application for Fault Diagnosis and Prediction,” in 2015 IEEE International Conference on Data Science and Data Intensive Systems, pp. 726-731, 2015.[24]M. F. Goodchild, “Geographical Data Modeling,” Computers & Geosciences, vol. 18, no. 4, pp. 401-408, 1992.[25]R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases,” in Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499, 1994.[26]J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” ACM SIGMOD Record, vol. 29, no. 2, pp. 1-12, 2000.[27]H. Lu, L. Feng, and J. Han, “Beyond Intratransaction Association Analysis: Mining Multidimensional Intertransaction Association Rules,” ACM Transactions on Information Systems, vol. 18, no. 4, pp. 423-454, 2000.[28]D. Brélaz, “New Methods to Color the Vertices of a Graph,” Communications of the ACM, vol. 22, no. 4, pp. 251-256, 1979. 描述 碩士
國立政治大學
資訊科學系
108753105資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108753105 資料類型 thesis dc.contributor.advisor 沈錳坤 zh_TW dc.contributor.author (Authors) 黃彥魁 zh_TW dc.contributor.author (Authors) Huang, Yen-Kuei en_US dc.creator (作者) 黃彥魁 zh_TW dc.creator (作者) Huang, Yen-Kuei en_US dc.date (日期) 2022 en_US dc.date.accessioned 5-Oct-2022 09:13:29 (UTC+8) - dc.date.available 5-Oct-2022 09:13:29 (UTC+8) - dc.date.issued (上傳時間) 5-Oct-2022 09:13:29 (UTC+8) - dc.identifier (Other Identifiers) G0108753105 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142118 - 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 zh_TW dc.description (描述) 108753105 zh_TW dc.description.abstract (摘要) 隨著網路的普及和感測器成本的降低，物聯網也逐漸興起。透過物聯網，可以監測不合預期的異常症狀。現有物聯網異常相關技術的研究，主要著重在異常事件的偵測，較少異常事件成因診斷的研究。導致異常事件的成因可能是觀測環境的異常或儀器設備本身的異常。針對異常成因的診斷，現有研究都集中在網路攻擊的異常事件。本論文以環境物聯網為例，研究物聯網異常事件診斷的方法。我們歸納整理空汙環境物聯網的異常症狀、時空線索與成因，提出物聯網異常事件診斷的方法與流程。根據我們所提出的診斷流程，設計實作一個異常事件診斷系統，提供使用者透過聚焦、歸納、對比，逐步地由眾多可能的線索中，探索出最可能導致異常事件的成因。本論文並以真實案例，實證我們所提出的系統可以協助使用者方便有效地找出異常成因。 zh_TW dc.description.abstract (摘要) With the spread of the internet and the cost reduction of sensors, the Internet of Things (IoT) became more popular. People can monitor and detect unexpected anomaly symptoms using IoT. Most of the existing research focuses on anomaly event detection while little research has been paid to the anomaly event diagnosis. Anomaly event may come from the deviation in environment or malfunction of devices themselves. Most current work on anomaly event diagnosis aim at the malicious attacks in IoT network.This thesis investigated the method of anomaly event diagnosis using environmental IoT as an example. We organized anomaly symptoms, temporal clues, spatial clues, and the root causes of anomaly events in environmental IoT for air pollution. This thesis also proposed the method and the procedure to diagnose anomaly events. According to the proposed procedure, this thesis designed and implemented an anomaly diagnosis system. The system provides the ability to focus, organize and compare the clues for anomaly diagnosis. It helps users to rule out unlikely root causes and explore possible root causes that triggered anomaly events. The proposed approach is demonstrated by real cases to show that the system could assist users to explore the root causes of anomaly events conveniently and effectively. en_US dc.description.tableofcontents 致謝 i摘要 iiAbstract iii目錄 iv圖目錄 vi表目錄 ix第一章 緒論 11.1 研究背景 11.2 研究動機與目的 2第二章 相關研究 42.1 IoT Data 42.2 Anomaly Detection 52.3 Diagnosis 6第三章 研究方法 83.1 物聯網異常診斷 83.2 系統設計 143.2.1 資料 153.2.2 Symptom Definition 163.2.3 Frequent Itemset Mining 193.2.4 Discriminative Pattern Mining 213.2.5 Visualization 24第四章 系統實作 284.1 資料 284.1.1 資料來源 284.1.2 資料前處理 294.2 系統介面 304.2.1 Observation Data 304.2.2 Symptom Definition 314.2.3 Anomaly Event Identification 334.2.4 Temporal Clue Discovery 364.2.5 Spatial Clue Discovery 384.3 系統後端 43第五章 案例分析 445.1 案例一 445.2 案例二 485.3 案例三 53第六章 結論 61參考文獻 62 zh_TW dc.format.extent 5060676 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108753105 en_US dc.subject (關鍵詞) 物聯網 zh_TW dc.subject (關鍵詞) 異常診斷 zh_TW dc.subject (關鍵詞) 樣式探勘 zh_TW dc.subject (關鍵詞) Internet of Things(IoT) en_US dc.subject (關鍵詞) Anomaly diagnosis en_US dc.subject (關鍵詞) Pattern mining en_US dc.title (題名) 物聯網異常診斷平台：以環境物聯網為例 zh_TW dc.title (題名) Anomaly Diagnostic Platform for IoT: Using Environmental IoT as an Example en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1]C. Tsai, C. Lai, M. Chiang, and L. T. Yang, “Data Mining for Internet of Things: A Survey,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 77-97, 2014.[2]F. Chen, P. Deng, J. Wan, D. Zhang, A. V. Vasilakos, and X. Rong, “Data Mining for the Internet of Things: Literature Review and Challenges,” International Journal of Distributed Sensor Networks, vol. 11, no. 8, 2015.[3]G. Atluri, A. Karpatne, and V. Kumar, “Spatio-Temporal Data Mining: A Survey of Problems and Methods,” ACM Computing Surveys, vol. 51, no. 4, pp. 1-41, 2018.[4]O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. H. D. N. Hindia, “An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges,” IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3758-3773, 2018.[5]A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of Things for Smart Cities,” IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22-32, 2014.[6]R. Jurdak, X. R. Wang, O. Obst, and P. Valencia, "Wireless Sensor Network Anomalies: Diagnosis and Detection Strategies," Intelligence-Based Systems Engineering, Intelligent Systems Reference Library, pp. 309-325, 2011.[7]V. Chandola, A. Banerjee, and V. Kumar, “Anomaly Detection: A Survey,” ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, 2009.[8]V. P. Illiano and E. C. Lupu, “Detecting Malicious Data Injections in Wireless Sensor Networks: A Survey,” ACM Computing Surveys, vol. 48, no. 2, pp. 1-33, 2015.[9]A. A. Cook, G. Mısırlı, and Z. Fan, “Anomaly Detection for IoT Time-Series Data: A Survey,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6481-6494, 2020.[10]L. Erhan, M. Ndubuaku, M. Di Mauro, W. Song, M. Chen, G. Fortino, O. Bagdasar, and A. Liotta, “Smart Anomaly Detection in Sensor Systems: A Multi-perspective Review,” Information Fusion, vol. 67, pp. 64-79, 2021.[11]M. Fahim and A. Sillitti, “Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review,” IEEE Access, vol. 7, pp. 81664-81681, 2019.[12]M. Hasan, M. M. Islam, M. I. I. Zarif, and M. M. A. Hashem, “Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches,” Internet of Things, vol. 7, 2019.[13]I. Alrashdi, A. Alqazzaz, E. Aloufi, R. Alharthi, M. Zohdy, and H. Ming, “AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning,” in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, pp. 305-310, 2019.[14]Z. Chen, L. Tian, and C. Lin, "A Method for Detection of Anomaly Node in IOT," Algorithms and Architectures for Parallel Processing, Lecture Notes in Computer Science, pp. 777-784, 2015.[15]Z. Deng, D. Weng, J. Chen, R. Liu, Z. Wang, J. Bao, Y. Zheng, and Y. Wu, “AirVis: Visual Analytics of Air Pollution Propagation,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 800-810, 2020.[16]L. Chen, Y. Ho, H. Hsieh, S. Huang, H. Lee, and S. Mahajan, “ADF: An Anomaly Detection Framework for Large-Scale PM2.5 Sensing Systems,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 559-570, 2018.[17]A. Rodrigues, T. Camilo, J. S. Silva, and F. Boavida, “Diagnostic Tools for Wireless Sensor Networks: A Comparative Survey,” Journal of Network and Systems Management, vol. 21, no. 3, pp. 408-452, 2013.[18]S. Chou, H. Yen, and A. Pang, “A REM-Enabled Diagnostic Framework in Cellular-Based IoT Networks,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5273-5284, 2019.[19]D. Rodenas-Herráiz, P. R. A. Fidler, T. Feng, X. Xu, S. Nawaz, and K. Soga, “A Handheld Diagnostic System for 6LoWPAN Networks,” in 2017 13th Annual Conference on Wireless On-demand Network Systems and Services, pp. 104-111, 2017.[20]A. Mahapatro and P. M. Khilar, “Fault Diagnosis in Wireless Sensor Networks: A Survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2000-2026, 2013.[21]D. Li, Y. Wang, J. Wang, C. Wang, and Y. Duan, “Recent Advances in Sensor Fault Diagnosis: A Review,” Sensors and Actuators A: Physical, vol. 309, 2020.[22]Z. Zhang, A. Mehmood, L. Shu, Z. Huo, Y. Zhang, and M. Mukherjee, “A Survey on Fault Diagnosis in Wireless Sensor Networks,” IEEE Access, vol. 6, pp. 11349-11364, 2018.[23]C. Wang, H. T. Vo, and P. Ni, “An IoT Application for Fault Diagnosis and Prediction,” in 2015 IEEE International Conference on Data Science and Data Intensive Systems, pp. 726-731, 2015.[24]M. F. Goodchild, “Geographical Data Modeling,” Computers & Geosciences, vol. 18, no. 4, pp. 401-408, 1992.[25]R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases,” in Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499, 1994.[26]J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” ACM SIGMOD Record, vol. 29, no. 2, pp. 1-12, 2000.[27]H. Lu, L. Feng, and J. Han, “Beyond Intratransaction Association Analysis: Mining Multidimensional Intertransaction Association Rules,” ACM Transactions on Information Systems, vol. 18, no. 4, pp. 423-454, 2000.[28]D. Brélaz, “New Methods to Color the Vertices of a Graph,” Communications of the ACM, vol. 22, no. 4, pp. 251-256, 1979. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202201643 en_US
