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SHF: Small: Towards Obfuscation-Resilient Software Plagiarism Detection

SHF：小：迈向抗混淆软件抄袭检测

基本信息

批准号：
1320605
负责人：
Sencun Zhu
金额：
$ 50万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2013
资助国家：
美国
起止时间：
2013-08-01 至 2017-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320605&HistoricalAwards=false
关键词：
SHF Small Towards Obfuscation Resilient

项目摘要

Software plagiarism is an act of reusing someone else's code, in whole or in part, into one own program in a way violating the terms of original license. Along with the rapid developing software industry and the burst of open source projects, software plagiarism has become a very serious threat to Intellectual Property Protection and the "healthiness" of the open-source-embracing software industry. Meanwhile, software plagiarism (and what is called app repackaging) has become an even more common phenomenon in the mobile app markets for monetary profit or propagation of malware by inserting malicious payloads into the original apps. To address this threat, computer-aided, automated plagiarism detection tools should play a major role. However, existing plagiarism detection schemes are not resilient to code obfuscation, and often they can be defeated by (in most cases rather simple) code-obfuscation-based counter-detection tools which are freely available.In this project, the software plagiarism detection problem is studied in a systematic way. The proposed plagiarism detection methods for PC applications leverage program logic and longest semantically-equivalent-basic-block subsequences. They are capable of detecting partial program plagiarism and also provide formal guarantee on obfuscation resilience. The proposed method for mobile apps exploits user interface for plagiarism detection, and this unique design angle empowers it to defeat various code obfuscation techniques. Our proposed research will significantly deter the intention or practice of software plagiarism. It will not only serve as a useful tool in collecting strong plagiarism evidences for lawsuits related to intellectual property, but also promote a more healthy and trustworthy sharing environment for the open source community and for the mobile app markets. Broader impact will also result from the education and dissemination initiatives.

软件剽窃是一种以违反原始许可条款的方式将他人代码的全部或部分重用到自己的程序中的行为。沿着软件产业的快速发展和开源项目的大量涌现，软件剽窃已经成为对知识产权保护和开源软件产业“健康”发展的严重威胁。与此同时，软件剽窃（以及所谓的应用程序重新打包）已经成为移动的应用程序市场中更为常见的现象，通过将恶意有效载荷插入原始应用程序来获取金钱利润或传播恶意软件。为了应对这一威胁，计算机辅助的自动剽窃检测工具应该发挥重要作用。然而，现有的剽窃检测方案是没有弹性的代码混淆，他们往往可以击败（在大多数情况下，相当简单）基于代码混淆的反检测工具，这是免费的。提出的PC应用程序的剽窃检测方法利用程序逻辑和最长语义等效的基本块顺序。它们能够检测部分程序剽窃，并提供形式化的模糊恢复保证。所提出的方法为移动的应用程序利用用户界面的剽窃检测，这种独特的设计角度使其能够击败各种代码混淆技术。我们提出的研究将显着阻止软件剽窃的意图或做法。它不仅将作为一个有用的工具，为知识产权相关的诉讼收集强有力的剽窃证据，而且还将为开源社区和移动的应用程序市场促进一个更健康、更值得信赖的共享环境。教育和宣传活动也将产生更广泛的影响。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Cryptographic Function Detection in Obfuscated Binaries via Bit-Precise Symbolic Loop Mapping

DOI：
10.1109/sp.2017.56
发表时间：
2017-05
期刊：
2017 IEEE Symposium on Security and Privacy (SP)
影响因子：
0
作者：
Dongpeng Xu;Jiang Ming;Dinghao Wu
通讯作者：
Dongpeng Xu;Jiang Ming;Dinghao Wu

Software Protection on the Go: A Large-Scale Empirical Study on Mobile App Obfuscation

DOI：
10.1145/3180155.3180169
发表时间：
2018-05
期刊：
2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)
影响因子：
0
作者：
Pei Wang;Qinkun Bao;Li Wang;Shuai Wang;Zhaofeng Chen;Tao Wei;Dinghao Wu
通讯作者：
Pei Wang;Qinkun Bao;Li Wang;Shuai Wang;Zhaofeng Chen;Tao Wei;Dinghao Wu

VMHunt: A Verifiable Approach to Partially-Virtualized Binary Code Simplification

DOI：
10.1145/3243734.3243827
发表时间：
2018-10
期刊：
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
影响因子：
0
作者：
Dongpeng Xu;Jiang Ming;Yu Fu;Dinghao Wu
通讯作者：
Dongpeng Xu;Jiang Ming;Yu Fu;Dinghao Wu

DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing