CroLSSim: Cross-language software similarity detector using hybrid approach of LSA-based AST-MDrep features and CNN-LSTM model

被引：0

作者：

Ullah, Farhan ^{[1
]}

Naeem, Muhammad Rashid ^{[2
]}

Naeem, Hamad ^{[3
]}

Cheng, Xiaochun ^{[4
]}

Alazab, Mamoun ^{[5
]}

机构：

[1] School of Software, Northwestern Polytechnical University, Jiangsu, Taicang,215400, China

[2] School of artificial intelligence, Leshan Normal University, Sichuan, Leshan,614000, China

[3] School of Computer Science and Technology, Zhoukou Normal University, Henan, Zhoukou,466000, China

[4] Department of Computer Science, Middlesex University, London,NW4 4BT, United Kingdom

[5] College of Engineering, IT and Environment at Charles Darwin University, Casuarina,NT,10095, Australia

来源：

International Journal of Intelligent Systems | 2022年 / 37卷 / 09期

关键词：

Compendex;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

C++ (programming language) - Syntactics - Semantics - Software design - Application programs - Decision trees - Extraction - Inverse problems - Long short-term memory - Open systems - Text processing - Open source software

引用

页码：5768 / 5795

共 2 条

[1] CroLSSim: Cross-language software similarity detector using hybrid approach of LSA-based AST-MDrep features and CNN-LSTM model
Ullah, Farhan
Naeem, Muhammad Rashid
Naeem, Hamad
Cheng, Xiaochun
Alazab, Mamoun
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2022, 37 (09) : 5768 - 5795
[2] Novel approach for Arabic fake news classification using embedding from large language features with CNN-LSTM ensemble model and explainable AI
Omar Ibrahim Aboulola
Muhammad Umer
Scientific Reports, 14 (1)

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