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Automatic code generation based on abstract syntax-based encoding. Application on malware detection code generation based on MITRE ATT&CK techniques

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 264卷

作者： Sirbu, Alexandru-Gabriel Czibula, Gabriela Babes Bolyai Univ Fac Math & Comp Sci Dept Comp Sci 1M Kogalniceanu St Cluj Napoca 400084 Romania

In the last decade, the area of code generation based on natural language was one of the most studied machine learning topics. The paper addresses the problem of code generation from natural language, by generating a syntax-error-free generator model, which creates an syntax-based model, later translated into code, for generating the structure of the code. Two approaches are comparatively investigated for generating the structure of the program. The first approach generates code templates in the form of an abstract syntax Tree, while the second generates the code in the form of an abstract syntax graph, anew introduced concept which reduces the initial redundancy of abstract syntax Trees and uses it as anew way to generate code. The proposed methodology is tested on two literature data sets and on malware detection code generation based on areal data set containing MITRE ATT&CK techniques. The results outperform the state-of-the-art abstract syntax Tree approaches by 2.46% and the plain text-based approaches with more than 12.5%, highlighting that the proposed methodology learns better the structural representation than other literature approaches.

关键词： Deep learning Recurrent neural network abstract syntax tree abstract syntax graph Malware detection

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There Are Infinite Ways to Formulate Code: How to Mitigate the Resulting Problems for Better Software Vulnerability Detection

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INFORMATION 2024年第4期15卷

作者： Groppe, Jinghua Groppe, Sven Senf, Daniel Moeller, Ralf Univ Lubeck Inst Informat Syst IFIS Ratzeburger Allee 160 D-23562 Lubeck Germany Lufthansa Ind Solut AS GmbH Schutzenwall 1 D-22844 Norderstedt Germany

Given a set of software programs, each being labeled either as vulnerable or benign, deep learning technology can be used to automatically build a software vulnerability detector. A challenge in this context is that there are countless equivalent ways to implement a particular functionality in a program. For instance, the naming of variables is often a matter of the personal style of programmers, and thus, the detection of vulnerability patterns in programs is made difficult. Current deep learning approaches to software vulnerability detection rely on the raw text of a program and exploit general natural language processing capabilities to address the problem of dealing with different naming schemes in instances of vulnerability patterns. Relying on natural language processing, and learning how to reveal variable reference structures from the raw text, is often too high a burden, however. Thus, approaches based on deep learning still exhibit problems generating a detector with decent generalization properties due to the naming or, more generally formulated, the vocabulary explosion problem. In this work, we propose techniques to mitigate this problem by making the referential structure of variable references explicit in input representations for deep learning approaches. Evaluation results show that deep learning models based on techniques presented in this article outperform raw text approaches for vulnerability detection. In addition, the new techniques also induce a very small main memory footprint. The efficiency gain of memory usage can be up to four orders of magnitude compared to existing methods as our experiments indicate.

关键词： software security software vulnerability deep learning 3-property encoding variable name dependence abstract syntax graph

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Variables are a Curse in Software Vulnerability Prediction 34th

Variables are a Curse in Software Vulnerability Prediction

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34th International Conference on Database and Expert Systems Applications (DEXA)

作者： Groppe, Jinghua Groppe, Sven Moeller, Ralf Univ Lubeck Inst Informat Syst IFIS Ratzeburger Allee 160 D-23562 Lubeck Germany

ISBN: (纸本)9783031398469;9783031398476

Deep learning-based approaches for software vulnerability prediction currently mainly rely on the original text of software code as the feature of nodes in the graph of code and thus could learn a representation that is only specific to the code text, rather than the representation that depicts the 'intrinsic' functionality of a program hidden in the text representation. One curse that causes this problem is an infinite number of possibilities to name a variable. In order to lift the curse, in this work we introduce a new type of edge called name dependence, a type of abstract syntax graph based on the name dependence, and an efficient node representation method named 3-property encoding scheme. These techniques will allow us to remove the concrete variable names from code, and facilitate deep learning models to learn the functionality of software hidden in diverse code expressions. The experimental results show that the deep learning models built on these techniques outperform the ones based on existing approaches not only in the prediction of vulnerabilities but also in the memory need. The factor of memory usage reductions of our techniques can be up to the order of 30,000 in comparison to existing approaches.

关键词： deep learning software security software vulnerability abstract syntax graph 3-property encoding name dependence

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Name Resolution Strategies in Variability Realization Languages for Software Product Lines 2016

Name Resolution Strategies in Variability Realization Langua...

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7th International Workshop on Feature-Oriented Software Development (FOSD)

作者： Schuster, Sven Nieke, Michael Schaefer, Ina Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

ISBN: (纸本)9781450346474

Software Product Lines (SPLs) exploit reuse-in-the-large to enable customization by explicitly modeling commonalities and variabilities of closely related software systems. Different approaches exist to enable SPL development and product creation by using modular languages, such as Feature-Oriented Programming (FOP) (compositional) or Delta-Oriented Programming (DOP) (transformational). These languages incorporate, e.g., object-oriented languages, adding a layer of variability. Creating a variability-aware abstract syntax graph (ASG), i.e., an ASG that contains the complete variability of the SPL, facilitates family-based analyses and is essential for supporting developers during SPL development. To this end, name resolution has to be performed. However, name resolution for these languages is a challenge as multiple declarations for the same element may occur in different modules. In this paper, we propose four name resolution strategies for compositional and transformational SPL realization languages and discuss their benefits and drawbacks, categorized by relevant application scenarios of the ASG.

关键词： Software Product Lines Feature-Oriented Programming Delta-Oriented Programming abstract syntax graph name resolution

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