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系統識別號 U0026-2001202116514500
論文名稱(中文) 通過機器學習檢測物聯網的分散式阻絕服務攻擊
論文名稱(英文) Detecting DDoS Attacks for IoT through Machine Learning
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 109
學期 1
出版年 110
研究生(中文) 陳俊佑
研究生(英文) Chun-Yu Chen
學號 P76074436
學位類別 碩士
語文別 英文
論文頁數 41頁
口試委員 指導教授-蔡孟勳
口試委員-蘇淑茵
口試委員-蔡佩璇
口試委員-陳盈如
口試委員-周詩梵
中文關鍵字 分散式阻絕服務攻擊  軟體定義網路  機器學習  循環神經網路 
英文關鍵字 DDoS  SDN  Machine Learning  Recurrent Neural Network 
學科別分類
中文摘要 隨著有線和無線通信技術的進步,物聯網設備也在不斷增加。
其中一種造成分散式阻絕服務攻擊發生原因是駭客入侵大量缺乏安全保護的物聯網設備,使設備成為殭屍網絡,並命令其攻擊特定的主機或服務。
我們採用軟體定義網路架構並新增分散式阻絕服務攻擊檢測模塊的來管理並收集物聯網所有設備的輸入輸出封包資訊。
在軟體定義網路架構管理的幫助下,基於flow的分散式阻絕服務檢測方法更適用於物聯網。

本文提出了一種在物聯網中,以不同機器學習模型在flow中加入更多時間步(timestep)架構來檢測物聯網的分散式阻絕服務攻擊。
我們使用私有物聯網測試資料集中發現在五個時間步長(5-timestep)和三元組(3-tuple)索引的bi-GRU模型,其精度達到100%。
我們從其他論文推薦在分散式阻絕服務攻擊偵測的機器學習模型中選擇了四個出色的模型,發現隨機森林和bi-GRU模型均達到了100%的準確度。
此外,如果將三元組索引轉換為來源IP位址,目標IP子網域和協定號碼,則在未分散式阻絕服務攻擊的準確性可達80%的檢測準確度。
英文摘要 With the advancement of wired and wireless communication technologies, the growth of Internet of Things (IoT) devices is also increasing.
Hackers exploit huge amount of IoT devices, which lack security protection for specific purposes.
To ease the problem, we adopt the SDN architecture to manage the IoT devices with DDoS detection module.
Distributed denial of service (DDoS) attack is an enhanced denial of service (DoS) attack, and is one of common usages of these hacked devices.
With over 20 years history of development of Detection of DoS or DDoS attacks, the flow-based method is more suitable for IoT.

In this paper, we propose a timestep architecture in differnet machine learning model and suitable model and parameters in IoT.
We find the bi-GRU model with 5 timesteps (25s) and 3-tuple index achieve 100\% accuracy in the private NTHU IoT testing dataset.
We select the 4 outstanding models from the related work and find the random forest and bi-GRU model have achieve 100\% accuracy.
In addition, the accuracy in unknown DDoS attack is up to 80\% detection accuracy if we transform the flows 3-tuple formats into source IP, destination subnet, and protocol number.
論文目次 Introduction 1
Related Work 4
Proposed Method 10
Performance Evaluation 18
Conclusion 34
References 35
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