ANALYSIS OF MACHINE LEARNING METHODS FOR AUTOMATING PENETRATION TESTING

Automation of penetration testing using machine learning methods is one of the most promising areas in modern cybersecurity. The traditional approach to penetration testing requires significant resources, including financial ones, as well as the involvement of highly qualified specialists capable of conducting a comprehensive assessment of system security. This approach may not always provide sufficient speed in detecting new threats, especially in the face of the ever-increasing complexity of cyberattacks and the large number of vulnerabilities. The introduction of machine learning methods into the pentesting process allows creating flexible, adaptive systems that can not only automate routine tasks but also increase the accuracy and efficiency of vulnerability detection. This article provides an overview of the key machine learning algorithms that can be used to automate penetration testing, including support vector machines, random forest, naive Bayes, decision trees, and reinforcement learning methods. Each of these algorithms offers certain advantages in the context of vulnerability analysis, threat classification, and prioritisation of critical security issues. Special attent