<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The escalating complexity and sophistication of software vulnerabilities demand innovative approaches in cybersecurity. This study introduces a groundbreaking framework, named “CodeSentry”, employing a transformer-based model for vulnerability detection in software code. “CodeSentry” leverages a finely-tuned version of the Generative Pre-trained Transformer (GPT), optimized for pinpointing vulnerable code patterns across various benchmark datasets. This approach stands apart by its remarkable computational efficiency, making it suitable for real-time applications − a significant advancement over traditional, resource-intensive deep learning models like CNNs and LSTMs. Empirical results showcase “CodeSentry” achieving an impressive 92.65% accuracy in vulnerability detection, surpassing existing state-of-the-art methods such as SyseVR and VulDeBERT. This novel methodology marks a paradigm shift in vulnerability detection, blending advanced AI with practical application efficiency.</p>
</abstract>

The escalating complexity and sophistication of software vulnerabilities demand innovative approaches in cybersecurity. This study introduces a groundbreaking framework, named “CodeSentry”, employing a transformer-based model for vulnerability detection in software code. “CodeSentry” leverages a finely-tuned version of the Generative Pre-trained Transformer (GPT), optimized for pinpointing vulnerable code patterns across various benchmark datasets. This approach stands apart by its remarkable computational efficiency, making it suitable for real-time applications − a significant advancement over traditional, resource-intensive deep learning models like CNNs and LSTMs. Empirical results showcase “CodeSentry” achieving an impressive 92.65% accuracy in vulnerability detection, surpassing existing state-of-the-art methods such as SyseVR and VulDeBERT. This novel methodology marks a paradigm shift in vulnerability detection, blending advanced AI with practical application efficiency.

Codesentry: Revolutionizing Real-Time Software Vulnerability Detection With Optimized GPT Framework

The University of Virginia, Virginia, USA & Capitol Technology University

Land Forces Academy Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The escalating complexity and sophistication of software vulnerabilities demand innovative approaches in cybersecurity. This study introduces a groundbreaking...

Codesentry: Revolutionizing Real-Time Software Vulnerability Detection With Optimized GPT Framework

Online veröffentlicht: 28. Feb. 2024

Seitenbereich: 98 - 107

DOI: https://doi.org/10.2478/raft-2024-0010

Schlüsselwörter
CodeSentry, generative pre-trained language models, software vulnerability detection, software security, GPT2, advanced AI security

© 2024 Angel Jones et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Codesentry: Revolutionizing Real-Time Software Vulnerability Detection With Optimized GPT Framework

Online veröffentlicht: 28. Feb. 2024

Seitenbereich: 98 - 107

DOI: https://doi.org/10.2478/raft-2024-0010

SchlüsselwörterCodeSentry, generative pre-trained language models, software vulnerability detection, software security, GPT2, advanced AI security

© 2024 Angel Jones et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
CodeSentry, generative pre-trained language models, software vulnerability detection, software security, GPT2, advanced AI security