Idioma:

Authorship attribution of source code by using back propagation neural network based on particle swarm optimization

Yang, Xinyu ; Xu, Guoai ; Li, Qi ; Guo, Yanhui ; Zhang, Miao Deng, Yong

PloS one, 2017-11, Vol.12 (11), p.e0187204-e0187204 [Periódico revisado por pares]

United States: Public Library of Science

Texto completo disponível

Citações Citado por

Enviar para

Título:
Authorship attribution of source code by using back propagation neural network based on particle swarm optimization
Autor: Yang, Xinyu ; Xu, Guoai ; Li, Qi ; Guo, Yanhui ; Zhang, Miao
Deng, Yong
Assuntos: Accuracy ; Algorithms ; Analysis ; Artificial intelligence ; Artificial neural networks ; Authoring ; Authorship ; Back propagation ; Back propagation networks ; Biology and Life Sciences ; C plus plus ; Classification ; Codes ; Communications networks ; Computer and Information Sciences ; Computer security ; Cybersecurity ; Data encryption ; Engineering ; Engineering and Technology ; Fault diagnosis ; Forensic sciences ; Java ; Java (programming language) ; Language ; Methods ; Neural networks ; Optimization ; Particle swarm optimization ; Physical Sciences ; Plagiarism ; Programmers ; Programming Languages ; Propagation ; Social Sciences ; Source code ; Swarm intelligence ; Syntax
É parte de: PloS one, 2017-11, Vol.12 (11), p.e0187204-e0187204
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
Descrição: Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead.
Editor: United States: Public Library of Science
Idioma: Inglês

Links

View this record in MEDLINE/PubMed

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

Authorship attribution of source code by using back propagation neural network based on particle swarm optimization

Yang, Xinyu ; Xu, Guoai ; Li, Qi ; Guo, Yanhui ; Zhang, Miao Deng, Yong

United States: Public Library of Science

Buscando em bases de dados remotas. Favor aguardar.