Research on Intelligent Mining and Repair of Network Security Vulnerabilities based on Deep Learning

Abstract

With the rapid advancement of information technology, network systems have grown exponentially in scale, leading to a surge
in vulnerabilities. Traditional vulnerability mining and remediation techniques have become inadequate for meeting modern cybersecurity
demands. Deep learning, with its exceptional capabilities in feature extraction and pattern recognition, offers innovative solutions for intelligent vulnerability detection and repair. This paper first analyzes the research background and limitations of traditional vulnerability mining
approaches, then elaborates on key deep learning technologies for vulnerability mining, including code representation methods, vulnerability
detection model construction, and vulnerability localization techniques. It further explores deep learning-driven vulnerability remediation
techniques, covering solution generation and effectiveness evaluation mechanisms. Finally, the paper summarizes current technical challenges
and outlines future development trends, providing valuable references for related research in the field.