Codesentry: Revolutionizing Real-Time Software Vulnerability Detection With Optimized GPT Framework-Reference-Cited by-同舟云学术

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

Published:2024-02-28 Issue:1 Volume:29 Page:98-107
ISSN:2247-840X
Container-title:Land Forces Academy Review
language:en
Short-container-title:

Author:

Jones Angel¹,Omar Marwan²

Affiliation:

1. The University of Virginia, Virginia, USA & Capitol Technology University , Maryland , USA

2. Capitol Technology University , Maryland, USA & Illinois Institute of Technology , Chicago , USA

Abstract

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.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/raft-2024-0010

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