Literature review on vulnerability detection using NLP technology
- URL: http://arxiv.org/abs/2104.11230v1
- Date: Fri, 23 Apr 2021 03:16:51 GMT
- Title: Literature review on vulnerability detection using NLP technology
- Authors: Jiajie Wu
- Abstract summary: Vulnerability detection has always been the most important task in the field of software security.
This article does a brief survey of some recent new documents and technologies, such as CodeBERT, and summarizes the previous technologies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Vulnerability detection has always been the most important task in the field
of software security. With the development of technology, in the face of
massive source code, automated analysis and detection of vulnerabilities has
become a current research hotspot. For special text files such as source code,
using some of the hottest NLP technologies to build models and realize the
automatic analysis and detection of source code has become one of the most
anticipated studies in the field of vulnerability detection. This article does
a brief survey of some recent new documents and technologies, such as CodeBERT,
and summarizes the previous technologies.
Related papers
- AI-Based Software Vulnerability Detection: A Systematic Literature Review [6.604556571951421]
This study presents a systematic review of software vulnerability detection (SVD) research from 2018 to 2023.<n>Our analysis reveals that 91% of studies use AI-based methods, with graph-based models being the most prevalent.<n>We identify key limitations, including dataset quality, interpretability, and highlight emerging opportunities in underexplored techniques.
arXiv Detail & Related papers (2025-06-12T01:42:38Z) - Automating the Detection of Code Vulnerabilities by Analyzing GitHub Issues [6.6681265451722895]
We introduce a new dataset specifically designed for classifying GitHub issues relevant to vulnerability detection.
Results demonstrate the potential of this approach for real-world application in early vulnerability detection.
This work has the potential to enhance the security of open-source software ecosystems.
arXiv Detail & Related papers (2025-01-09T14:13:39Z) - The Good, the Bad, and the (Un)Usable: A Rapid Literature Review on Privacy as Code [4.479352653343731]
Privacy and security are central to the design of information systems endowed with sound data protection and cyber resilience capabilities.
Developers often struggle to incorporate these properties into software projects as they either lack proper cybersecurity training or do not consider them a priority.
arXiv Detail & Related papers (2024-12-21T15:30:17Z) - SCoPE: Evaluating LLMs for Software Vulnerability Detection [0.0]
This work explores and refines the CVEFixes dataset, which is commonly used to train models for code-related tasks.
The output generated by SCoPE was used to create a new version of CVEFixes.
The results show that SCoPE successfully helped to identify 905 duplicates within the evaluated subset.
arXiv Detail & Related papers (2024-07-19T15:02:00Z) - Patch2QL: Discover Cognate Defects in Open Source Software Supply Chain
With Auto-generated Static Analysis Rules [1.9591497166224197]
We propose a novel technique for detecting cognate defects in OSS through the automatic generation of SAST rules.
Specifically, it extracts key syntax and semantic information from pre- and post-patch versions of code.
We have implemented a prototype tool called Patch2QL and applied it to fundamental OSS in C/C++.
arXiv Detail & Related papers (2024-01-23T02:23:11Z) - Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors [57.7003399760813]
We explore advanced Large Language Models (LLMs) and their specialized variants, contributing to this field in several ways.
We uncover a significant correlation between topics and detection performance.
These investigations shed light on the adaptability and robustness of these detection methods across diverse topics.
arXiv Detail & Related papers (2023-12-20T10:53:53Z) - Towards Possibilities & Impossibilities of AI-generated Text Detection:
A Survey [97.33926242130732]
Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses.
Despite these advancements, several works in the existing literature have raised serious concerns about the potential misuse of LLMs.
To address these concerns, a consensus among the research community is to develop algorithmic solutions to detect AI-generated text.
arXiv Detail & Related papers (2023-10-23T18:11:32Z) - A Survey on Automated Software Vulnerability Detection Using Machine
Learning and Deep Learning [19.163031235081565]
Machine Learning (ML) and Deep Learning (DL) based models for detecting vulnerabilities in source code have been presented in recent years.
It may be difficult to discover gaps in existing research and potential for future improvement without a comprehensive survey.
This work address that gap by presenting a systematic survey to characterize various features of ML/DL-based source code level software vulnerability detection approaches.
arXiv Detail & Related papers (2023-06-20T16:51:59Z) - Can AI-Generated Text be Reliably Detected? [50.95804851595018]
Large Language Models (LLMs) perform impressively well in various applications.
The potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their responsible use.
We stress-test the robustness of these AI text detectors in the presence of an attacker.
arXiv Detail & Related papers (2023-03-17T17:53:19Z) - Developing Hands-on Labs for Source Code Vulnerability Detection with AI [0.0]
We propose a framework including learning modules and hands on labs to guide future IT professionals towards developing secure programming habits.
This thesis our goal is to design learning modules with a set of hands on labs that will introduce students to secure programming practices using source code and log file analysis tools.
arXiv Detail & Related papers (2023-02-01T20:53:58Z) - A Hierarchical Deep Neural Network for Detecting Lines of Codes with
Vulnerabilities [6.09170287691728]
Software vulnerabilities, caused by unintentional flaws in source codes, are the main root cause of cyberattacks.
We propose a deep learning approach to detect vulnerabilities from their LLVM IR representations based on the techniques that have been used in natural language processing.
arXiv Detail & Related papers (2022-11-15T21:21:27Z) - VELVET: a noVel Ensemble Learning approach to automatically locate
VulnErable sTatements [62.93814803258067]
This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code.
Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph.
VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
arXiv Detail & Related papers (2021-12-20T22:45:27Z) - Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
We propose a novel selection-and-weighting-based anomaly detection framework called SWAD.
Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
arXiv Detail & Related papers (2021-03-08T10:56:38Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - Survey of Network Intrusion Detection Methods from the Perspective of
the Knowledge Discovery in Databases Process [63.75363908696257]
We review the methods that have been applied to network data with the purpose of developing an intrusion detector.
We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods.
As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
arXiv Detail & Related papers (2020-01-27T11:21:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.