Research on Cyberspace Intrusion Detection Model for Wireless Agile Network Planning

As the Internet technology rapidly advances, the world is transitioning into an information society with a growing amount of valuable data in cyberspace. Consequently, ensuring the security and reliability of cyberspace has become a critical challenge. Although traditional intrusion detection techniques have shown effectiveness, they suffer from drawbacks such as slow detection speed, low accuracy, and high false alarm rates. To address these issues, this paper presents a novel intrusion detection model, the Neuron-mapping-based Inception Convolutional Neural Network (NICNN), based on deep learning technology. Experimental results reveal impressive accuracy rates of 99.56% and 99.24% on the 10% KDD99 data set and KDDTRAIN + data set, respectively, along with remarkably low false alarm rates of 0.15% and 0.22%. Compared to existing intrusion detection methods, NICNN exhibits superior detection performance.