The Struggle with Academic Plagiarism: Approaches based on Semantic Similarity
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Academic plagiarism is a serious problem nowadays. Due to the existence of inexhaustible sources of digital information, today it is easier to plagiarize more than ever before. The good thing is that plagiarism detection techniques have improved and are powerful enough to detect attempts of plagiarism in education. We are now witnessing efficient plagiarism detection software in action, such as Turnitin, iThenticate or SafeAssign. In the introduction we explore software that is used within the Croatian academic community for plagiarism detection in universities and/or in scientific journals. The question is: is this enough? Current software has proven to be successful, however the problem of identifying paraphrasing or obfuscation plagiarism remains unresolved. In this paper we present a report of how semantic similarity measures can be used in the plagiarism detection task.Keywords:
Plagiarism detection
Obfuscation
Similarity (geometry)
Academic community
Obfuscation
Code (set theory)
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The aim of obfuscation in general is to prevent malicious users from disclosing properties of the original source program. This goal can be achieved by an intermediate level obfuscation that deals with a target platform independent intermediate code. In this paper, we discuss general approaches to an intermediate level obfuscation algorithm, pointing out problems and proposing solutions. The paper discusses such aspects of intermediate level obfuscation as input data analysis, mixing of contexts, external function calls, etc. The focus is set on working out an optimization resistant intermediate level obfuscation algorithm that can reliably protect routines from unauthorized analysis and modification.
Obfuscation
Code (set theory)
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As the obfuscation is widely used by malware writers to evade antivirus scanners, so it becomes important to analyze how this technique is applied to malwares. This paper explores the malware obfuscation techniques while reviewing the encrypted, oligomorphic, polymorphic and metamorphic malwares which are able to avoid detection. Moreover, we discuss the future trends on the malware obfuscation techniques.
Obfuscation
Cryptovirology
Malware analysis
Ransomware
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Obfuscation
Plagiarism detection
Code (set theory)
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Plagiarism detection
Similarity (geometry)
Empirical Research
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Academic plagiarism is a serious problem nowadays. Due to the existence of inexhaustible sources of digital information, today it is easier to plagiarize more than ever before. The good thing is that plagiarism detection techniques have improved and are powerful enough to detect attempts of plagiarism in education. We are now witnessing efficient plagiarism detection software in action, such as Turnitin, iThenticate or SafeAssign. In the introduction we explore software that is used within the Croatian academic community for plagiarism detection in universities and/or in scientific journals. The question is - is this enough? Current software has proven to be successful, however the problem of identifying paraphrasing or obfuscation plagiarism remains unresolved. In this paper we present a report of how semantic similarity measures can be used in the plagiarism detection task.
Plagiarism detection
Obfuscation
Similarity (geometry)
Academic community
Cite
Citations (19)
Using unevaluated obfuscation methods has a significant risk since the methods might have some vulnerabilities. One evaluation for obfuscation is de-obfuscation which discloses the hidden information by the obfuscation. This paper proposed the de-obfuscation method against for DNR (dynamic name resolution) obfuscation method. DNR hides system-defined names by encrypting them and resolves names dynamically during runtime. This paper clarifies the steps of de-obfuscation and proposes static and dynamic manners to de-obfuscate DNR. Through the case study, two ways both succeed in disclosing the hidden information of DNR.
Obfuscation
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There exist many plagiarism detection tools to uncover plagiarized codes by analyzing the similarity of source codes. To measure how reliable those plagiarism detection tools are, we developed a tool named Code ObfuscAtion Tool (COAT) that takes a program source code as input and produces another source code that is exactly equivalent to the input source code in their functional behaviors but with a different structure. In COAT, we particularly considered the eight representative obfuscation techniques (e.g., modifying control flow or inserting dummy codes) to test the performance of source code plagiarism detection tools. To show the practicality of COAT, we gathered 69 source codes and then tested those source codes with the four popularly used source code plagiarism detection tools (Moss, JPlag, SIM and Sherlock). In these experiments, we found that the similarity scores between the original source codes and their obfuscated plagiarized codes are very low; the mean similarity scores only ranged from 4.00 to 16.20 where the maximum possible score is 100. These results demonstrate that all the tested tools have clear limitations in detecting the plagiarized codes generated with combined code obfuscation techniques.
Obfuscation
Plagiarism detection
Code (set theory)
Similarity (geometry)
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Cases of plagiarism in recent years has been an issues. Based on that issues, this research will create a system to detect similarity in a text. There is an aspect as reference of the research that is analyze the plagiarism algorithm. This research will analyze the accuracy one of plagiarism check algorithm, winnowing algorithm. Winnowing algorithm is a plagiarism detection algorithm based on document fingerprinting. To calculate percentage similarity of document fingerprinting in text, there are 3 methods to measure similarity that will be used in this research, which is jaccard similarity coefficient, sorensen dice similarity coefficient, and berg similarity coefficient.
Jaccard index
Similarity (geometry)
Plagiarism detection
Winnowing
Similarity measure
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In this paper we address the issue of defining security of program obfuscation. We argue that requirements to obfuscated programs may be different and are dependent on potential applications. Therefore three distinct models are suggested for studying the issues of obfuscation, namely obfuscation for software protection, total obfuscation and constant hiding. We also introduce a definition of weak obfuscation based on “grey-box” paradigm and show this weak form of obfuscation to be impossible.
Obfuscation
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