Abstract
The existing traditional network attack detection algorithms have low detection accuracy and are difficult to cope with external malicious and capricious network attacks in the communication and control systems. In order to solve this problem, this paper designs a network attack detection method based on convolutional neural network technology, which improves the recognition accuracy of attack detection for network attack signals. Specifically, a convolutional neural network structure with overlapping pooling layers is designed, and the improved data preprocessing method and parameter tuning make the recognition accuracy of the obtained convolution network on the KDD99 data set greatly improved. Finally, the experimental results show that the proposed network tuning strategy is effective, and the neural network structure designed in this paper is superior to other existing intelligent detection algorithms in terms of recognition accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
