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研究生:陳昱任
研究生(外文):Yu-JenChen
論文名稱:物聯網環境應用非監督式學習之異常偵測系統
論文名稱(外文):An Anomaly Detection System Based on Unsupervised Learning in IoT Environment
指導教授:陳敬陳敬引用關係
指導教授(外文):Jing Chen
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:108
語文別:中文
論文頁數:165
中文關鍵詞:異常偵測物聯網非監督式學習
外文關鍵詞:Anomaly DetectionInternet of ThingIoTUnsupervised Learning
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近年隨著半導體、網路通訊之迅速發展,使物聯網系統的應用蓬勃發展。物聯網系統的運作與效益,仰賴其內部眾多感測器所產生的大量數據資料。對蒐集之資料進行進一步的資料分析,例如:視覺資料辨識、語音辨識、安全等級評估、異常偵測與預警等等,使物聯網系統的應用範圍與功能性提高。其中,異常偵測與預警是物聯網結合數據資料分析領域中的重要應用。
本論文透過研究物聯網架構與異常偵測演算法,設計可適用於非特定場域,以提供異常偵測功能之物聯網管理系統。本論文研究與設計之重點包含:(1)採用RESTful設計風格之管理系統,以增加本系統之管理性;(2)異常偵測部分採用模組化介面設計,目的為提供本物聯網系統內多樣化之異常偵測演算法同時運行之設計;(3)服務與管理群組的功能,以及多感測器情境的設計。本系統內可存在多種多個感測器,透過群組管理以及多工管理設計可達到多重感測器協同運行以及複數異常偵測演算法同時運作。
為驗證本系統之設計可用性,本論文研究目前常見異常偵測演算法之運行模式與架構,並考量物聯網環境資料特性,選擇適用於無標籤資料並可串流運作之機器學習演算法:階層式時序性記憶模型(Hierarchical Temporal Memory, HTM)作為本論文異常偵測驗證之範例模型。
本論文主要貢獻為一非監督式異常偵測系統暨物聯網管理系統,此系統可應用於不同場域,並針對該場域進行客製化設定;且選用之範例異常偵測模型在無須提供複雜環境設定下即可針對該場域之數據特性進行自動學習與判斷。模組化設計可在無須修改系統運作流程與實作的情況下,擴充本系統內之異常偵測演算法種類。實作採用容器化技術,可在僅增加微小運算成本的情況下,大幅提高系統可移植性與保證系統完整性。
Anomaly detection intents to find a set of unusual data points in seen data which might suggest system error, warning of critical status of system or rare situation that user attention is needed in order to response early or to handle properly so that further damage of system can be prevented. Traditional anomaly detection methods work fine with fixed dataset but might be unsuitable for datasets generated in IoT environment, which are in general streaming, heterogeneity in format, and concept drifting datasets. Requiring domain knowledge and resource constraints are also challenges in adopting classic anomaly detection methods in IoT application. Further, with the advances in the domain of nowadays widespread machine learning, achieving automatic and intelligent anomaly detection in IoT environment is highly desirable.

This study aims to design and implement a system capable of managing the devices in IoT environment and integrated with a anomaly detection system based on unsupervised machine learning technique. The main achievement and contribution of this study is a well-designed, fully working IoT management and anomaly detection system with the desirable features including REST (Representational State Transfer) user interfaces and a framework of anomaly detection functionality. For the purposes of demonstration and verification, a prototype system is built and tested. The results of testing cases showed that this system is able to manage the devices deployed in an IoT environment and detecting anomaly effectively. With the module of anomaly detection framework, this system is both flexible and expandable.
第1章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究方法 4
1.4 章節規劃 4
第2章 相關研究 5
2.1 物聯網系統之介紹與比較 5
2.1.1 物聯網應用 5
2.1.2 物聯網架構 6
2.2 表現層狀態轉換架構設計 10
2.3 適用於物聯網環境之異常偵測演算法介紹與比較 12
2.3.1 物聯網資料特性 12
2.3.2 異常偵測介紹 14
2.3.3 適用於物聯網環境之異常偵測 21
第3章 系統架構與設計 26
3.1 使用情境 26
3.2 異常偵測模組使用情境 28
3.3 服務群組與異常偵測使用情境 30
3.4 架構與框架設計 32
3.4.1 感測器子框架 34
3.4.2 數據端子框架 36
3.4.3 智聯網子框架 39
3.5 異常偵測模組 45
3.6 異常偵測模組之介面設計 49
3.6.1 執行期模組類別 49
3.6.2 物聯網感測器靜態資訊類別 56
3.6.3 感測器感測資料類別 58
3.6.4 執行期模組控制指令類別 59
第4章 系統實作 61
4.1 實作環境 61
4.2 系統內資源關聯與RESTful架構實現 63
4.2.1 資源物件構成與關聯 63
4.2.2 RESTful架構實現 65
4.2.3 訂閱機制通知 73
4.3 架構實作 74
4.4 系統流程實作 76
4.4.1 請求處理流程 78
4.4.2 推送請求處理流程 79
4.4.3 獲取請求處理流程 88
4.4.4 更新請求處理流程 94
4.4.5 刪除請求處理流程 96
4.5 異常偵測模組運作行為 104
4.5.1 感測器之感測資料處理流程 104
4.5.2 執行期模組資料處理流程 108
4.5.3 執行期模組控制處理流程 113
4.5.4 聯合異常偵測處理流程 115
第5章 測試與成果 117
5.1 測試環境 117
5.2 功能性測試 118
5.2.1 功能測試範例 118
5.2.2 功能性測試流程與成果 120
5.3 異常偵測功能 142
5.3.1 異常偵測演算法驗證 142
5.3.2 訂閱機制驗證 151
5.4 討論 153
第6章 結論與展望 154
6.1 結論 154
6.2 未來展望 154
第7章 參考資料 155
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