Active Learning for Language Identification with N-gram Technique

Kate, Yuxin Feng

doi:10.1109/icbase53849.2021.00110

2021 2nd International Conference on Big Data &Amp; Artificial Intelligence &Amp; Software Engineering (ICBASE) 2021

DOI: 10.1109/icbase53849.2021.00110

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Active Learning for Language Identification with N-gram Technique

Yuxin Feng Kate

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2024

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NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity

Wu,

Weng,

Zheng

et al. 2024

J Software Evolu Process

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As software grows in size and complexity, software vulnerabilities are increasing, leading to a range of serious insecurity issues. Open‐source software vulnerability reports and documentation can provide researchers with great convenience for analysis and detection. However, the quality of different data sources varies, the data are duplicated and lack of correlation, which often requires a lot of manual management and analysis. In order to solve the problems of scattered and heterogeneous data and lack of correlation in traditional vulnerability repositories, this paper proposes a software vulnerability feature knowledge extraction method that combines the N‐gram model and mask similarity. The method generates mask text data based on the extraction of N‐gram candidate keywords and extracts vulnerability feature knowledge by calculating the similarity of mask text. This method analyzes the samples efficiently and stably in the environment of large sample size and complex samples and can obtain high‐value semi‐structured data. Then, the final node, relationship, and attribute information are obtained by secondary knowledge cleaning and extraction of the extracted semi‐structured data results. And based on the extraction results, the corresponding software vulnerability domain knowledge graph is constructed to deeply explore the semantic information features and entity relationships of vulnerabilities, which can help to efficiently study software security problems and solve vulnerability problems. The effectiveness and superiority of the proposed method is verified by comparing it with several traditional keyword extraction algorithms on Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) vulnerability data.

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NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity

Wu,

Weng,

Zheng

et al. 2024

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

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Active Learning for Language Identification with N-gram Technique

Cited by 1 publication

References 4 publications

NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity

NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity

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