ML-Powered Insight Into Code Software Vulnerability

Abstract

As software systems grow increasingly complex,
ensuring their security becomes paramount.
Vulnerabilities in software can lead to devastating
consequences, including data breaches, system
compromise, and financial losses. Traditional
methods of detecting vulnerabilities rely heavily on
manual code inspection, which is time-consuming
and error-prone. In recent years, machine learning
(ML) algorithms have emerged as promising tools
for automating the detection of software
vulnerabilities.
This research proposes a novel software vulnerability
detection tool that leverages machine learning
algorithms. The tool utilizes supervised learning
techniques to analyze code repositories and identify
potential vulnerabilities. By training on labeled
datasets of known vulnerabilities, the system learns
to recognize patterns indicative of security flaws.

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