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Author: 黃俊維
Thesis Title: 以AI驅動評估DDoS網路攻擊的風險架構
An AI Driven Risk Assess Framework to Evaluate DDoS Cyber Attacks
Advisor: 賴源正
Yuan-Cheng Lai
Committee: 賴源正
Yuan-Cheng Lai
Nai-Wei Lo
Yen-Hung Chen
Degree: 碩士
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2019
Graduation Academic Year: 107
Language: 英文
Pages: 42
Keywords (in Chinese): 機器學習物聯網卷積神經網路分散式阻斷服務攻擊
Keywords (in other languages): Machine Learning, Internet of Things, Convolutional Neural Networks, Distributed Denial-of-Service
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  • 雲端運算(Cloud computing)和物聯網(Internet of Things, IoT)已成為滿足未來企業需求的兩項關鍵技術。然而,大規模的分散式阻斷服務攻擊(Distributed Denial-of-Service, DDoS)已被廣泛應用於壅塞關鍵連結(Target Links)並使雲端和物聯網服務癱瘓。這主要是因為DDoS是透過發動大規模合法的低流量逐步使目標區域網路癱瘓。藉此有許多管理框架來評估DDoS影響的風險指標。然而,這些風險指標都缺乏時間粒度來評估物聯網或大規模網路結構中不同攻擊規模的成本。本研究提出了一種名為ADE (AI Driven Evaluation)的AI驅動評估框架,它應用卷積神經網路(Convolution Neural Networks)透過端到端功能(輸入:網路狀態;輸出:檢測結果)評估網路狀態,無需任何人工干預。ADE透過使用學習時間作為控制變量,網路結構作為自變量,以及將DDoS識別所需的時間作為因變量來提供量化的安全風險分析。然後應用檢測DDoS事件的偵測時間來評估DDoS的規模,及當前網路拓撲的脆弱性。實驗結果表明,ADE的貢獻是:(1)提供客觀和量化的分析安全風險評估指標;(2)提供自主DDoS防禦框架,無需任何人工干預,允許雲端計算和物聯網公司專注於他們的服務並由ADE提供安全保護,以及(3)證明AI輔助安全風險評估的可能性,使處理安全防禦方案的企業能夠減少安全領域專家,來評估合適的網路防禦策略。

    The cloud computing and Internet of Things (IoT) have become two key technologies to meet future business requirements. However, a massive scale of Distributed Denial-of-Service (DDoS) has been widely applied to congest network critical links and to paralyze the cloud and IoT service. This is mainly due to DDoS is easily implemented, obfuscated, and occulted by launching large-scale legitimate low-speed flows and rolling target links to paralyze target network areas. Many metrics and risk access management frameworks to evaluate the impact of DDoS are proposed. However, they all lack time granularity to evaluate the cost of different scales of attacks in IoT or large-scale network structure. This study proposes an AI Driven Evaluation framework, called ADE, that applies Convolution Neural Networks to statistically evaluate the network status through end-to-end functionality (Input: network status; Output: detected result) without any manual intervention. ADE provides quantitative security risk analysis by using learning time as the control variable, network structure as the independent variable, and time to identify DDoS as the dependent variable. The learning time to detect DDoS event is then applied to evaluate the scale of this DDoS, the reasonability of the regulated detection time, and the vulnerability of the current network topology. The experiment results demonstrate the contributions of ADE are (1) providing objective and quantitative analytical security risk assessment indicator, (2) providing an autonomic DDoS defense framework without any manual intervention which allows cloud computing and Internet of Things company focuses on their service and leaves security defending to ADE, and (3) demonstrating the possibility of AI assisted risk assessment which enables security defense solution buyer with less security domain experts to evaluate suitable network defense strategy.

    摘要 Abstract Contents Lists of Figures Lists of Tables Chapter 1 Introduction Chapter 2 Risk Assessment Procedure Chapter 3 Problem Description Chapter 4 AI Driven Evaluation framework Chapter 5 Experiment Result Chapter 6 Conclusion References

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