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Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents

JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING 2020年第2期32卷 42-66页

作者： Zaher, Mahmoud Shehab, Abdulaziz Elhoseny, Mohamed Farahat, Farahat Farag Mansoura Univ Fac Comp & Informat Mansoura Egypt Sadat Acad Cairo Egypt

Due to the rapid increase of internet-based data, there is urgent need for a robust intelligent documents security mechanism. Although there are many attempts to build a plagiarism detection system in natural language documents, the unlimited variation and different writing styles of each character in Arabic documents make building such systems challenging. Based on its position in a word, the same Arabic letter can be written three different ways, which makes the handwritten character recognition a cumbersome process. This article proposes an intelligent unsupervised model to detect plagiarism in these documents called ASTAP. First, a handwritten Arabic character recognition system is proposed using the Grey Wolf Optimization (GWO) algorithm. Then, a modified abstract syntax tree (AST) is used to match the contents of the Arabic documents to detect any similarity. Compared to the state-of-the-art methods, ASTAP improves the effectiveness of the plagiarism detection in terms of the matched similarity ratio, the precision ratio, and the processing time.

关键词： abstract syntax tree Gray Wolf Optimization Handwritten Character Recognition Hash value Internet Data Security Plagiarism Detection Similarity Index Unsupervised Documents Analysis

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Semantic Feature Learning via Dual Sequences for Defect Prediction

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IEEE ACCESS 2021年 9卷 13112-13124页

作者： Lin, Junhao Lu, Lu South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Modern Ind Technol Res Inst Meizhou 528400 Peoples R China

Software defect prediction (SDP) can help developers reasonably allocate limited resources for locating bugs and prioritizing their testing efforts. Existing methods often serialize an abstract syntax tree (AST) obtained from the program source code into a token sequence, which is then inputted into the deep learning model to learn the semantic features. However, there are different ASTs with the same token sequence, and it is impossible to distinguish the tree structure of the ASTs only by a token sequence. To solve this problem, this paper proposes a framework called Semantic Feature Learning via Dual Sequences (SFLDS), which can capture the semantic and structural information in the AST for feature generation. Specifically, based on the AST, we select the representative nodes in the AST and convert the program source code into a simplified AST (S-AST). Our method introduces two sequences to represent the semantic and structural information of the S-AST, one is the result of traversing the S-AST node in pre-order, and another is composed of parent nodes. Then each token in the dual sequences is encoded as a numerical vector via mapping and word embedding. Finally, we use a bi-directional long short-term memory (BiLSTM) based neural network to automatically generate semantic features from the dual sequences for SDP. In addition, to leverage the statistical characteristics contained in the handcrafted metrics, we also propose a framework called Defect Prediction via SFLDS (DP-SFLDS) which combines the semantic features generated from SFLDS with handcrafted metrics to perform SDP. In our empirical studies, eight open-source Java projects from the PROMISE repository are chosen as our empirical subjects. Experimental results show that our proposed approach can perform better than several state-of-the-art baseline SDP methods.

关键词： Semantics Measurement Logic gates Software Feature extraction Software metrics Vegetation Software defect prediction abstract syntax tree deep learning bi-directional long short-term memory network

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Mining Variable-Method Correlation for Change Impact Analysis

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IEEE ACCESS 2018年 6卷 77581-77595页

作者： Hu, Chunling Li, Bixin Sun, Xiaobing Hefei Univ Dept Comp Sci & Technol Hefei 230026 Anhui Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Jiangsu Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou 225009 Jiangsu Peoples R China

Software change impact analysis (CIA) is a key technique to identify the potential ripple effects of the changes to software. Coarse-grained CIA techniques such as file, class and method level techniques often gain less precise change impacts, which are difficult for practical use. Fine-grained CIA techniques, such as slicing, can be used to gain more precise change impacts, but need more time and space cost. In this paper, by combining the features of the coarse-grained technique and the fine-grained technique, a variable-method (VM) correlation-based CIA technique called VM-CIA is proposed to improve the precision of static CIA. First, the VM-CIA technique uses the abstract syntax tree (AST) of program to construct a novel intermediate representation called variable and method triple (VMT), which is used to analyze the correlation between the variables and methods. Second, the VM-CIA technique proposes the single-change impact analysis algorithm and multi-change impact analysis algorithm to compute the impact set based on the VMT representation. In addition, the VM-CIA technique can get a sorted impact set which is more accurate than the existing CIA techniques. The empirical results show that the VM-CIA technique can greatly improve the precision (19%) over traditional the CIA techniques, while at the cost of a little recall (5%). Moreover, the empirical studies also show that the VM-CIA technique predicts a ranked list of potential impact results according to the distance measure, which can greatly facilitate the practical use.

关键词： Change impact analysis variable-method correlation variable and method triple impact propagation call graph impact set abstract syntax tree

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BovdGFE: buffer overflow vulnerability detection based on graph feature extraction

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APPLIED INTELLIGENCE 2023年第12期53卷 15204-15221页

作者： Lv, Xinghang Peng, Tao Chen, Jia Liu, Junping Hu, Xinrong He, Ruhan Jiang, Minghua Cao, Wenli Wuhan Text Univ Sch Comp Sci & Artificial Intelligence Wuhan 430200 Hubei Peoples R China Hubei Prov Engn Res Ctr Intelligent Text & Fash Wuhan 430200 Hubei Peoples R China Engn Res Ctr Hubei Prov Clothing Informat Wuhan 430200 Hubei Peoples R China

Automatically detecting buffer overflow vulnerabilities is an important research topic in software security. Recent studies have shown that vulnerability detection performance utilizing deep learning-based techniques can be significantly enhanced. However, due to information loss during code representation, existing approaches cannot learn the features associated with vulnerabilities, leading to a high false negative rate (FNR) and low precision. To resolve the existing problems, we propose a method for buffer overflow vulnerability detection based on graph feature extraction (BovdGFE) in C/C++ programs. BovdGFE constructs the buffer overflow function samples. Then, we present a new representation structure, code representation sequence (CoRS), which incorporates the control flow, data dependencies, and syntax structure of the vulnerable code for reducing information loss during code representation. After the function samples are transformed into CoRS, a deep learning model is used to learn vulnerable features and perform vulnerability classification. The results of the experiments show that BovdGFE improves the precision and FNR by 6.3% and 3.9% respectively compared with state-of-the-art methods, which can significantly improve the capability of vulnerability detection.

关键词： Vulnerability detection abstract syntax tree Control flow graph Data dependency graph Code representation Deep learning

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A graph-based code representation method to improve code readability classification

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EMPIRICAL SOFTWARE ENGINEERING 2023年第4期28卷 1-26页

作者： Mi, Qing Zhan, Yi Weng, Han Bao, Qinghang Cui, Longjie Ma, Wei Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

Context Code readability is crucial for developers since it is closely related to code maintenance and affects developers' work efficiency. Code readability classification refers to the source code being classified as pre-defined certain levels according to its readability. So far, many code readability classification models have been proposed in existing studies, including deep learning networks that have achieved relatively high accuracy and good performance. Objective However, in terms of representation, these methods lack effective preservation of the syntactic and semantic structure of the source code. To extract these features, we propose a graph-based code representation method. Method Firstly, the source code is parsed into a graph containing its abstract syntax tree (AST) combined with control and data flow edges to reserve the semantic structural information and then we convert the graph nodes' source code and type information into vectors. Finally, we train our graph neural networks model composing Graph Convolutional Network (GCN), DMoNPooling, and K-dimensional Graph Neural Networks (k-GNNs) layers to extract these features from the program graph. Result We evaluate our approach to the task of code readability classification using a Java dataset provided by Scalabrino et al. (2016). The results show that our method achieves 72.5% and 88% in three-class and two-class classification accuracy, respectively. Conclusion We are the first to introduce graph-based representation into code readability classification. Our method outperforms state-of-the-art readability models, which suggests that the graph-based code representation method is effective in extracting syntactic and semantic information from source code, and ultimately improves code readability classification.

关键词： Code readability classification Graph neural network Code representation abstract syntax tree Program comprehension

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Effective method for detecting malicious PowerShell scripts based on hybrid features

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NEUROCOMPUTING 2021年 448卷 30-39页

作者： Fang, Yong Zhou, Xiangyu Huang, Cheng Sichuan Univ Coll Cybersecur Chengdu Sichuan Peoples R China

At present, network attacks are rampant in the Internet world, and the attack methods of hackers are changing steadily. PowerShell is a programming language based on the command line *** framework, with powerful functions and good compatibility. Therefore, hackers often use PowerShell malicious scripts to attack the victims in APT attacks. When these malicious PowerShell scripts are executed, hackers can control the victim's computer or leave a backdoor on their computers. In this paper, a detection model of malicious PowerShell scripts based on hybrid features is proposed, we analyzed the differences between malicious and benign samples in text characters, functions, tokens and the nodes of the abstract syntax tree. Firstly, the script of PowerShell is embedded by FastText. Then the textual features, token features and the nodes features of PowerShell code extracted from the abstract syntax tree are added. Finally, the hybrid features of scrips will be classified by a Random Forest classifier. In the experiment, the malicious scripts are inserted into the benign scripts to weaken the features of the malicious samples in the level of abstract syntax tree nodes and tokens, which makes the scripts more complex. Even in such a complex data set, the proposed model which is based on hybrid features still achieves an accuracy of 97.76% in fivefold cross-validation. Moreover, the accuracy of this proposed model on the original scripts is 98.93%, which means that the proposed model has the ability to classify complex scripts. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Powershell abstract syntax tree Scripts detection Machine learning

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READSUM: Retrieval-Augmented Adaptive Transformer for Source Code Summarization

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IEEE ACCESS 2023年 11卷 51155-51165页

作者： Choi, Yunseok Na, Cheolwon Kim, Hyojun Lee, Jee-Hyong Sungkyunkwan Univ Dept Platform Software Suwon 16419 South Korea Sungkyunkwan Univ Dept Artificial Intelligence Suwon 16419 South Korea

Code summarization is the process of automatically generating brief and informative summaries of source code to aid in software comprehension and maintenance. In this paper, we propose a novel model called READSUM, REtrieval-augmented ADaptive transformer for source code SUMmarization, that combines both abstractive and extractive approaches. Our proposed model generates code summaries in an abstractive manner, taking into account both the structural and sequential information of the input code, while also utilizing an extractive approach that leverages a retrieved summary of similar code to increase the frequency of important keywords. To effectively blend the original code and the retrieved similar code at the embedding layer stage, we obtain the augmented representation of the original code and the retrieved code through multi-head self-attention. In addition, we develop a self-attention network that adaptively learns the structural and sequential information for the representations in the encoder stage. Furthermore, we design a fusion network to capture the relation between the original code and the retrieved summary at the decoder stage. The fusion network effectively guides summary generation based on the retrieved summary. Finally, READSUM extracts important keywords using an extractive approach and generates high-quality summaries using an abstractive approach that considers both the structural and sequential information of the source code. We demonstrate the superiority of READSUM through various experiments and an ablation study. Additionally, we perform a human evaluation to assess the quality of the generated summary.

关键词： Codes Transformers Source coding Syntactics Data mining Task analysis Adaptive systems Shortest path problem abstract syntax tree adaptive transformer source code summarization fusion network shortest path

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FAM: Featuring Android Malware for Deep Learning-Based Familial Analysis

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IEEE ACCESS 2022年 10卷 20008-20018页

作者： Ban, Younghoon Lee, Sunjun Song, Dokyung Cho, Haehyun Yi, Jeong Hyun Soongsil Univ Sch Software Convergence Seoul 06978 South Korea Soongsil Univ Sch Software Seoul 06978 South Korea Yonsei Univ Dept Comp Sci Seoul 03722 South Korea

To handle relentlessly emerging Android malware, deep learning has been widely adopted in the research community. Prior work proposed deep learning-based approaches that use different features of malware, and reported a high accuracy in malware detection, i.e., classifying malware from benign applications. However, familial analysis of real-world Android malware has not been extensively studied yet. Familial analysis refers to the process of classifying a given malware into a family (or a set of families), which can greatly accelerate malware analysis as the analysis gives their fine-grained behavioral characteristics. In this work, we shed light on deep learning-based familial analysis by studying different features of Android malware and how effectively they can represent their (malicious) behaviors. We focus on string features of Android malware, namely the abstract syntax trees (AST) of all functions extracted from each malware, which faithfully represent all string features of Android malware. We thoroughly study how different string features, such as how security-sensitive APIs are used in malware, affect the performance of our deep learning-based familial analysis model. A convolutional neural network was trained and tested in various configurations on 28,179 real-world malware dataset appeared in the wild from 2018 to 2020, where each malware has one or more labels assigned based on their behaviors. Our evaluation reveals how different features contribute to the performance of familial analysis. Notably, with all features combined, we were able to produce up to an accuracy of 98% and a micro F1-score of 0.82, a result on par with the state-of-the-art.

关键词： Malware Feature extraction Deep learning Analytical models Security Codes Training Malware classification deep learning Android security abstract syntax tree

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An AST Structure Enhanced Decoder for Code Generation

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2022年 30卷 468-476页

作者： Jiang, Hui Song, Linfeng Ge, Yubin Meng, Fandong Yao, Junfeng Su, Jinsong Xiamen Univ Sch Informat Xiamen 361005 Fujian Peoples R China Pengcheng Lab Shenzhen 518066 Guangdong Peoples R China Tencent Co Shenzhen 518000 Peoples R China Univ Illinois Urbana IL 61801 USA Tecent Co Shenzhen 100080 Peoples R China

Currently, the most dominant neural code generation modelsare often equipped with a tree-structured LSTM decoder, which outputs a sequence of actions to construct an abstract syntax tree (AST) via pre-order traversal. However, such a decoder has two obvious drawbacks. First, except for the parent action, other faraway and important history actions rarely contribute to the current decision. Second, it also neglects future actions, which may be crucial for the prediction of the current action. To deal with these issues, in this paper, we propose a novel AST structure enhanced decoder for code generation, which significantly extends the decoder with respect to the above two aspects. First, we introduce an AST information enhanced attention mechanism to fully exploit history actions, of which impacts are further distinguished according to their syntactic distances, action types and relative positions;Second, we jointly model the predictions of current action and its important future action via multi-task learning, where the learned hidden state of the latter can be further leveraged to improve the former. Experimental results on commonly-used datasets demonstrate the effectiveness of our proposed decoder.(1)

关键词： Codes Decoding Syntactics Predictive models History Grammar Speech processing Code generation abstract syntax tree attention mechanism future action prediction

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Bug localization based on syntactical and semantic information of source code

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Journal of Systems Engineering and Electronics 2023年第1期34卷 236-246页

作者： YAN Xuefeng CHENG Shasha GUO Liqin College of Computer Science Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 211106China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China

The existing software bug localization models treat the source file as natural language, which leads to the loss of syntactical and structure information of the source file. A bug localization model based on syntactical and semantic information of source code is proposed. Firstly, abstract syntax tree(AST) is divided based on node category to obtain statement sequence. The statement tree is encoded into vectors to capture lexical and syntactical knowledge at the statement ***, the source code is transformed into vector representation by the sequence naturalness of the statement. Therefore,the problem of gradient vanishing and explosion caused by a large AST size is obviated when using AST to the represent source code. Finally, the correlation between bug reports and source files are comprehensively analyzed from three aspects of syntax, semantics and text to locate the buggy code. Experiments show that compared with other standard models, the proposed model improves the performance of bug localization, and it has good advantages in mean reciprocal rank(MRR), mean average precision(MAP) and Top N Rank.

关键词： bug report abstract syntax tree code representation software bug localization

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