AI recommendation penetration testing tools for password attacks: random forest - Rozman, Nurulasyiqin; Saad, Shahadan (College of Computing, Informatics, and Mathematics, 2025)
This project developed an AI-based recommendation system using the Random Forest algorithm to address the complexities of selecting appropriate penetration testing tools for password attacks. Penetration testing, crucial for evaluating network and system security, faces challenges due to the variety of tools, especially for less experienced pentesters. The project's objective was to automate tool selection based on user-defined requirements, improving efficiency and effectiveness. Guided by the Extreme Programming (XP) methodology, the AI system analyzed attributes and requirements to provide personalized tool recommendations, such as Nmap, Medusa, Hydra, and Wfuzz, based on password attack types, targeted platforms, software types, hash types, and pentest goals. Implemented using Django and SQLite, the system reduced manual efforts and specialized knowledge needed for tool selection, allowing pentesters to focus on complex security tasks. The project's seamless integration with existing workflows demonstrated its practical capability and highlighted AI's potential in optimizing security practices, making pentesting more accessible for organizations with limited resources and expertise. By shifting focus from repetitive tasks to higher-level security analysis, the project enhanced organizational security against evolving cyber threats and showcased AI's role in improving cybersecurity practices.