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研究生: 唐錦為
Jin-Wei, Tang
論文名稱: 針對基於硬體實作網路入侵偵測系統效能評估之系統層級模擬架構
A System-Level Simulation Framework for Performance Evaluation of Hardware-Based Network Intrusion Detection Systems
指導教授: 鍾葉青
Yeh-Ching, Chung
口試委員:
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2007
畢業學年度: 95
語文別: 英文
論文頁數: 25
中文關鍵詞: 網路入侵偵測系統電子系統層級模式匹配
外文關鍵詞: intrusion detection system, electronic system level, pattern matching
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  • 網路入侵偵測系統是一套用來保護電腦不受到惡意使用者的攻擊的受歡迎的安全軟體。基於硬體實作的網路入侵偵測系統使用各種硬體加速器來增加效能,但是傳統的設計方法已經無法滿足設計複雜度快速成長的單晶片系統設計。在這篇論文,我們提出一個基於電子系統層級(Electronic System Level)軟體,ARM RealView SoC Designer的系統層級模擬架構。這個提出的模擬架構既可以更早地驗證網路入侵偵測系統,也可以更容易地評估各種不同架構的網路入侵偵測系統的效能。以這個模擬架構為基礎,我們實作兩個例子,一個是基於軟體實作的MyIDS;另一個是基於硬體實作的HASH HPMM(硬體模式匹配模組 hardware pattern matching module),並且和Snort比較效能。從實驗的結果可以得知,這個模擬架構可以很容易地評估網路入侵偵測系統的效能並且很快地找出較好的設計。


    The network intrusion detection system (NIDS) is a popular security tool to protect computers to from attacks by malicious users. A hardware-based NIDS uses several hardware accelerators to increase performance, but traditional design methodology does not satisfy the rapidly growing design complexity of SoC design. In this thesis, we propose a system-level simulation framework based on an ESL (Electronic System Level) tool, ARM RealView SoC Designer. The proposed framework can verify NIDS early and evaluate performance of various architectures easily. Based on this framework, we implement two cases, software-based MyIDS and hardware-based HASH HPMM (hardware pattern matching module), and compare their performance with Snort. The experimental results show that the simulation framework can evaluate performance of NIDS easily and find better design quickly.

    Chapter 1 Introduction 1 Chapter 2 Related Work 5 Chapter 3 Simulation Framework Overview 7 3.1 ARM RealView SoC Designer 8 3.2 System Architecture Description 9 3.3 Software NIDS Description 10 3.4 Hardware Accelerator - HPMM 11 Chapter 4 Implementation 12 4.1 Receive Packets 12 4.2 Case 1 – MyIDS 13 4.2.1 Four Layer Rule Chains 13 4.2.2 Boyer-Moore String Search Algorithm 14 4.3 Case 2 – HASH HPMM 15 Chapter 5 Experimental Results 16 5.1 Environment 16 5.2 Memory consumption 17 5.3 Detection results statistics 17 5.4 Performance evaluation 18 Chapter 6 Conclusions and Future Work 23 References 24

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