With the rapid development of the Internet of Things (IoT), cloud computing, and 5G communication technologies, network protocols, as the core of network communication, are facing increasingly severe security threats. Fuzz testing is an effective technology for detecting vulnerabilities in network protocols; however, traditional fuzz testing methods have limitations such as low test efficiency, blind test case generation, and difficulty in adapting to complex and diverse network protocols. In recent years, the rapid advancement of artificial intelligence (AI) technology has brought new opportunities for the innovation and development of network protocol fuzz testing. This paper systematically studies the application of AI technology in network protocol fuzz testing. First, it elaborates on the urgency of the current network security situation and the importance of network protocol fuzz testing. Then, it introduces the basic principles, core processes, and development stages of network protocol fuzz testing technology. Next, it focuses on the application methods of AI technologies such as machine learning, deep learning, and reinforcement learning in network protocol fuzz testing, and analyzes the improvement effects of AI on fuzz testing efficiency, coverage rate, and vulnerability detection accuracy. Subsequently, it explores the practical application scenarios of AI-based network protocol fuzz testing. Finally, it summarizes the current deficiencies of AI-based network protocol fuzz testing technology and looks forward to its future development trends. This study provides a theoretical reference and practical guidance for the research and application of network protocol fuzz testing technology in the AI era, helping to improve the security and reliability of network protocols.
原文链接
分享
引用
