Research on Network Information Security Algorithms based Intrusion Detection using Deep Learning
Kexian Pan · 2024
Network Information Security is a comprehensive field that safeguards computer networks and their data from unauthorized access, attacks, and damage through the use of various technologies and practices. An intrusion detection system (IDS) is essential for monitoring network activities and identifying suspicious or malicious behavior, thereby safeguarding the integrity, confidentiality, and availability of network information. The IDS system requires its effective performance and security to be improved. The objective of this study is to use the DL approach to implement an IDS. The most promising method for detecting intrusions is the DL model, which is also the most used. This work developed a convolutional Neural Network (SCSO-CNN) model for an intrusion detection system based on sand cat Swarm Optimization. Database of NSL-KDD is employed to estimate presented SCSO-CNN, and data is normalized using the pre-processing technique known as Min-Mix normalization. CNN is used to effectively and accurately categorize intrusion, whereas SCSO is used to effectively and accurately categorize intrusion, whereas SCSO is used to choose the characteristics. Using the NSL-KDD dataset, the developed SCSO-CNN obtains superior accuracy with 99.85% accuracy. From this work, utilization of an ID network concludes the achievement of high network information security and performance.