Artificial Intelligence-Driven Penetration Testing for Wireless Networks: Enhancing Security Vulnerability Detection Using CNN Models

Adware, spyware, viruses, and other forms of malware are serious risks to people, companies, governments, and military activities. Advanced methods for vulnerability detection are required since traditional security measures frequently fall short in the face of complex and dynamic cyberthreats. In order to increase accuracy, adaptability, and scalability in discovering security vulnerabilities, this study investigates the incorporation of artificial intelligence (AI) in the design of a wireless network penetration testing system, utilizing machine learning. The dataset used was BoTNeTIoT-L01, which has over 7 million records of IoT botnet attacks. Using the Keras library, a convolutional neural network (CNN) model with layers for convolution, max pooling, and dense was created. The Adam algorithm was then used to optimize the CNN model's training process. The model's remarkable 99.46% accuracy rate in categorizing assaults indicates how well it can detect security holes and adjusts to emerging threats. The results also confirm the capabilities of artificial intelligence in enhancing cybersecurity measures and ensuring strong protection in increasingly complex wireless network environments.