AI for Software Vulnerability Prediction: Integrating Static Code Analysis and Machine Learning for Robust Security Frameworks
Keywords:
Software vulnerabilities, static code analysis, machine learningAbstract
The increasing complexity of software systems has made software vulnerabilities a persistent challenge for security professionals. Predicting vulnerabilities before deployment can significantly enhance the security of software applications, and this is where Artificial Intelligence (AI) and machine learning can provide critical solutions. This paper explores the integration of static code analysis and machine learning for predicting software vulnerabilities. Static code analysis, a technique for examining the source code without executing the program, can identify potential flaws in the code structure, while machine learning algorithms can analyze historical vulnerability data to predict future risks. This integration offers a robust security framework, providing early identification of potential vulnerabilities and reducing the time and cost associated with security testing. We discuss the methodologies, challenges, and the future potential of using AI for vulnerability prediction, offering insights into how these technologies can strengthen modern software development practices.
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