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November 14, 2025ElectronicsOpen Access

Modern Approaches to Software Vulnerability Detection: A Survey of Machine Learning, Deep Learning, and Large Language Models

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Authors

MSMd. Shazzad Hossain ShaonMAMst Shapna AkterMSMd. Shazzad Hossain Shaon

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Overview

This survey finds that machine learning and deep learning improve vulnerability detection, highlighting interpretability and class imbalance challenges.

Key Points

  • Automated vulnerability detection enhances security and reliability in modern systems, and it is essential.
  • Machine learning models optimized through fine-tuning show significant improvements in feature interpretability.
  • Evaluation criteria for current vulnerability detection methods reveal persistent issues with class imbalance in datasets.
  • Future research may focus on neuro-symbolic approaches and parameter-efficient strategies for scalable solutions.

Cite This Study

Shaon et al. (2025) studied this question.

synapsesocial.com/papers/69251999c0ce034ddc353aa5https://doi.org/10.3390/electronics14224449
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  1. 1Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study2024 · 1 citations
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  4. 4Towards Effectively Detecting and Explaining Vulnerabilities Using Large Language Models2024 · 3 citations
  5. 5Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection2024