PDetect: A Clustering Approach for Detecting Plagiarism in Source Code Datasets

Moussiades, Lefteris; Vakali, Athena

doi:10.1093/comjnl/bxh119

Cited by 55 publications

(41 citation statements)

References 11 publications

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“…A clustering based approach, P-detect is used for detecting plagiarism in source datasets, developed by Lefteris Moussiades and Athena Vakali [18].This P-detect firstly take the set of programs as input and represents the programs as a set of keywords. A similarity measure evaluation by Jaccard"s similarity coefficient is performed for each pair of programs.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Plagiarism Detection of C Program using Assembly Language

Chauhan¹,

Arora²,

Singhal³

2017

IJCA

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Source code plagiarism is becoming a common practice among higher education community. People duplicate and modify the source code of other people and show the program as their own program. In this paper, we want to draw researchers' attention towards this problem and projected a novel approach which detects plagiarism in C language code by converting it into assembly language which is done with the help of GCC compiler. Assembly language converted by the compiler is not sensitive to all type of different code transformation, for example-swapping variable names, reformation of language, adding extra comment or blanks. Therefore, assembly language gives rise to reduced amount of variations, if there is a modification in the original code. Previous works in plagiarism compares the whole program but in this paper, we proposed a method which split the C program into assembly language code and divide each function of program into blocks and blocks are transformed into token strings. This method compares each function with other program function and provides a statistical output, according to the token string likeness of that function. If the output is above assigned specific plagiarism similarity threshold value then it counts under the case of plagiarism.

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Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Plagiarism Detection of C Program using Assembly Language

Chauhan¹,

Arora²,

Singhal³

2017

IJCA

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“…However, the winnowing algorithm requires calculating set inclusion, which is expensive when comparing many features. Additional plagiarism detection techniques are explored in [43][44][45], but they use complimentary techniques for plagiarism detection. Namely, source code clustering and manual analysis, program dependency graphs, and measuring approximate Kolmogorov complexity between programs, respectively.…”

Section: Related Workmentioning

confidence: 99%

Juxtapp: A Scalable System for Detecting Code Reuse among Android Applications

Hanna

Huang

et al. 2013

Detection of Intrusions and Malware, and Vulnerability Assessment

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131

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Abstract. Mobile application markets such as the Android Marketplace and the Amazon Android store provide a centralized showcase of applications that end users can purchase or download for free onto their mobile phones. Despite the influx of applications to the markets, applications are either largely unreviewed or only cursorily reviewed by marketplace maintainers due to the vast number of submissions; furthermore, they rely on user policing and reporting to detect misbehaving applications. This reactive approach to application security, especially when programs can contain bugs, malware, or pirated (inauthentic) code, puts too much responsibility on the end users. In light of this, we propose Juxtapp, a scalable infrastructure for code similarity analysis among Android applications. Juxtapp provides a key solution to a number of problems in Android security, including determining if apps contain copies of buggy code, have significant code reuse that indicates piracy, or are instances of known malware. We evaluate our system using more than 58,000 Android applications and demonstrate that our system scales well and is effective. Our results show that Juxtapp is able to detect: 1) 463 applications with confirmed buggy code reuse of Google-provided sample code that lead to serious vulnerabilities in real-world apps, 2) 34 instances of known malware and variants (including 13 distinct variants of the GoldDream malware), and 3) pirated variants of a popular paid game.

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“…A ideia desse método é baseada na proposta de representação e classificação de perfis de e [Oliveira et al 2014] e na estratégia de clusterização de plágios em exercícios de programação de [Moussiades and Vakali 2005].…”

Section: Fundamentação Teóricaunclassified

“…O PDetect é um sistema que faz análise e clusterização de plágios em exercícios de programação [Moussiades and Vakali 2005]. Para isso, o PDetect recebe códigos-fontes, mapeia-os em um conjunto de tokens de uma linguagem de programação, identifica os pares suspeitos de plágios e os reúne em clusters, formados a partir de um parâmetro de corte, que é o Coeficiente de Jaccard [Moussiades and Vakali 2005].…”

Section: Trabalhos Relacionadosunclassified

“…O trabalho de [Moussiades and Vakali 2005] utilizou o Clustering em Grafo para separar agrupamentos de plágios de exercícios de programação. Seguindo essa ideia para classificar os perfis gerados pelo PCodigo, estendemos a proposta de [Oliveira et al 2014] combinando a análise fatorial e o Clustering em Grafo para seleção não-aleatória dos conjuntos de treino dos modelos de classificação de perfis de estudantes de programação.…”

Section: Trabalhos Relacionadosunclassified

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Representação da Diversidade de Componentes Latentes em Exercícios de Programação para Classificação de Perfis

Oliveira

Jiménez

Daher

et al. 2015

Anais Do XXVI Simpósio Brasileiro De Informática Na Educação (SBIE 2015)

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Abstract. Using the factorial analysis technique, a set of factors or latent components can be discovered from quantitative variables to characterize programs written by students. Representing each program by a set of latent components, we call this representation of student profile. In order to classify students profiles, this paper proposes a non-random selection method based on Graph Clustering to select representative profiles from the diversity of latent components to build a training set for classification models. The results achieved demonstrate that the combination of factorial analysis with Graph Clustering improves outcomes of profiles classification.

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PDetect: A Clustering Approach for Detecting Plagiarism in Source Code Datasets

Cited by 55 publications

References 11 publications

Plagiarism Detection of C Program using Assembly Language

Plagiarism Detection of C Program using Assembly Language

Juxtapp: A Scalable System for Detecting Code Reuse among Android Applications

Representação da Diversidade de Componentes Latentes em Exercícios de Programação para Classificação de Perfis

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