CIRD: A Solution to Detect Real-time Zero-day Code-Injection Atttacks

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姓名

林鈺凱(Yu-Kai Lin) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(CIRD: A Solution to Detect Real-time Zero-day Code-Injection Atttacks)

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至系統瀏覽論文 (2024-6-30以後開放)

摘要(中)

在眾多的攻擊手法中，Buffer overflow 造成的Code-Injection 攻擊是
一種很嚴重的攻擊方式。因為攻擊者可以任意執行惡意程式碼，可能會
造成memory leak、任意記憶體位置讀寫、最嚴重可以拿到主機控制權。
本篇論文設計了一套偵測Code-Injection 的方式，利用QEMU 和
Linux Kernel 配合，可以即時偵測並且找出在執行檔哪個地方發生
Code-Injection。

摘要(英)

In many of attack methods, the Code-Injection attacks is a serious problem that makes attackers can execute malicious code arbitrarily. It may cause memory leak, arbitrarily memory read/write or even taking control on the host machine.
We had designed a method to detect Code-Injection attacks. Using QEMU and Linux Kernel, we can not only detect read-time Code-Injection attacks but also locate functions of Code-Injection vulnerability.

關鍵字(中)

★ 緩衝區溢位
★ 代碼注入

關鍵字(英)

★ Buffer overflow
★ Code-Injection

論文目次

摘要.................................................................................... i
Abstract .............................................................................. ii
誌謝.................................................................................... iii
目錄.................................................................................... iv
圖目錄................................................................................. vi
表目錄................................................................................. vii
第1 章緒論........................................................................ 1
第2 章背景介紹.................................................................. 2
2.1 Linux Process ID ........................................................ 2
2.2 Linux PID 分配機制.................................................... 3
2.3 QEMU TCG IR ......................................................... 4
2.4 Buffer overflow 與Shellcode 攻擊................................... 6
第3 章系統設計.................................................................. 8
3.1 系統架構.................................................................. 8
3.2 執行檔加工............................................................... 9
3.3 Guest OS kernel ......................................................... 11
3.4 QEMU 紀錄Assembly Code ......................................... 13
3.5 偵測Code-Injection 與注入點....................................... 14
第4 章實驗設計與實作......................................................... 15
4.1 測試環境.................................................................. 15
4.2 功能測試.................................................................. 15
4.3 效能測試.................................................................. 16
4.4 CVE 測試................................................................. 17
iv
目錄
第5 章相關研究.................................................................. 18
5.1 Memory Forensics ....................................................... 18
5.2 Convolutional Neural Network ....................................... 18
第6 章討論........................................................................ 19
第7 章總結........................................................................ 20
參考文獻.............................................................................. 21

參考文獻

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07/16/2019).
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(visited on 07/16/2019).
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Apr. 16, 2001. doi: 10.1109/ICDSC.2001.918971. [Online]. Available: https:
//ieeexplore.ieee.org/abstract/document/918971.
[4] (2019). Cve, [Online]. Available: https://www.exploit-db.com/exploits/17486
(visited on 07/16/2019).
[5] A. Srivastava, “Detecting code injection by cross-validating stack and vad information
in windows physical memory,” 2017 IEEE Conference on Open Systems
(ICOS), Nov. 13, 2017. doi: 10 . 1109 / ICOS . 2017 . 8280279. [Online]. Available:
https://ieeexplore.ieee.org/document/8280279.
[6] Y. Pan, J. An, W. Fan, and W. Huang, “A shellcode detection method based on
dynamic binary instrumentation and convolutional neural network,” ICSCA ’19
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Available: https://dl.acm.org/citation.cfm?id=3316731.
[7] LLVM 開發團隊. (2019). Llvm, [Online]. Available: https://llvm.org/ (visited on
07/16/2019).

指導教授

許富皓(Fu-Hau Hsu)

審核日期

2019-8-20

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