臺灣博碩士論文加值系統

隨著物聯網發展越來越成熟，相關的安全議題也逐漸受到重視，目前許多物聯網設備中會引入第三方函式庫來擴充功能。但如果函式庫中發生漏洞，經常會讓一系列的產品受到影響，當這些存在有弱點的設備暴露在網路上，很容易就成為駭客攻擊的目標，也導致研究人員在後續的修補和檢測將面臨一大挑戰。
研究人員在檢測設備時，主要會先從韌體開始。但韌體可能橫跨多種不同架構，會造成分析上的困難之外，檔案系統中複雜的結構也會增加分析上的成本，因此通常需要配合工具協助檢測。該如何有效的利用這些工具開始檢測，除了要對設備架構有一定的熟悉度之外，也仰賴研究人員的經驗。
本研究目的是開發一套自動化跨架構韌體漏洞檢測系統，將統整先前的研究方法，設計神經網路模型用來與漏洞程式碼進行相似度檢測，辨別出可能是危險的函式，同時也從韌體檔案系統中找尋敏感資訊，經過系統分析後會產生對應的結果報告，輔助研究人員能夠初步辨別目標設備可能存在的風險，降低需要耗費的時間成本。

As the prevalence of the Internet of Things and its flourishing advancement, many industries and organization has taken advantage of Internet of Thing in their daily operations, and the IoT security has become a significant issue. Nowadays, third-party libraries are usually imported to IoT device in order to expand their functionalities. However, once there are vulnerabilities in third-party libraries, many IoT devices will be influenced and more prone to cyber-attacks. Moreover, the widespread vulnerable third-party libraries will also be the adversities for researcher to detect and patch the system.
Firmware analysis is usually the primary method when examining IoT devices. However, the diversity of firmware architectures and humongous amounts of files in file system can procrastinate the progress of firmware analysis significantly. Therefore, conducting firmware analysis effectively requires researchers to possess certain sophisticated expertise and experiences.
To solve the abovementioned issues and mitigate workloads from researchers, this study developed an automated cross-platform firmware detection system. This study summarized previous methodology and design a neural network model to perform similarity check with vulnerable code segments. The proposed system can identify potential malicious function as well as discover sensitive information in file system. The analysis report can help researchers and investigators examine the IoT devices and discover embryonic security risks.

目錄
論文審定書 i
摘要 ii
Abstract iii
目錄 iv
圖次 vi
表次 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 5
第二章 文獻探討 7
2.1 韌體 7
2.2 動態分析 8
2.3 靜態分析 10
2.3 相似度檢測 11
2.4 控制流程圖(Control Flow Graph,CFG) 13
2.5 韌體分析工具 14
2.5.1 拆解工具 14
2.5.2 檢測工具 15
2.5.3 二進制檔分析工具 15
2.6 神經網路模型 17
2.6.1 深度神經網路(DNN) 18
2.6.2 孿生神經網路(Siamese Networks) 18
第三章 研究方法 20
3.1 韌體解壓縮模組 24
3.2 敏感資訊檢索模組 25
3.3 函式特徵提取模組 28
3.3.1. 屬性控制流程圖特徵 29
3.3.2.特徵預處理 32
3.4 檢測模組 34
3.4.1神經網路模型訓練 36
3.4.2 相似度計算 37
第四章 系統評估 38
4.1 實驗一 ACFG特徵提取方法比較 41
4.2 實驗二 相似度模型評估 44
4.3 實驗三 系統成效驗證 50
4.4 實驗四 韌體分析工具比較 52
第五章 研究貢獻與未來展望 55
參考文獻 56

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