Machine Learning Models for Detecting Software Vulnerabilities

Abstract

Pay close attention to finding vulnerabilities and making secure software, we need to become less vulnerable. Vulnerable software always gives significant chances for hackers to inject malicious SQL code and interfere with its functionality. Security groups attempt to identify weaknesses in software as early as possible in development to avoid losses costing software businesses millions of dollars. As a result, numerous reliable and efficient vulnerability identification models are needed for web applications, but for those websites, there is no proper mechanism to block or scan other input data. The hackers are injected through string commands and functions of SQL (Structure Query Language) due to the dynamic disregard of this command in the runtime and several ways to identify security flaws in software, such as supervised semi-supervised, ensemble, and deep learning, to the list of machine learning that can find vulnerabilities, but despite these models in the software industry.