Automated Penetration Testing Using Generative Artificial

Automated Penetration Testing Using Generative Artificial

연구 분야: Strategies

학회: International Conference on Global Security, Safety, and Sustainability

This research explores the integration of ChatGPT, a Large Language Model (LLM), with traditional penetration testing tools such as Nmap, Metasploit, Whois, and Msfvenom. By leveraging ChatGPT’s natural language processing abilities, the study introduces a novel approach to penetration testing that simplifies tool usage and improves task efficiency. The model assists in understanding complex queries, generating targeted attack scripts, and analyzing data. This fusion enhances both the accuracy and speed of penetration testing while making it more accessible to newcomers. The study also emphasizes the importance of ethical AI use in cybersecurity. The findings suggest that combining language models with technical tools fosters better communication among security experts and opens new possibilities for AI-assisted cybersecurity practices. Ultimately, the research paves the way for smarter, more efficient penetration testing methods as organizations strive to protect their digital assets.