Network Intrusion Automatic Detection Based on Mobile Wireless Network Application in Clothing Design Virtual Reality System

Abstract

The rapid advancement of mobile network technology has led to an increasing popularity of virtual reality (VR) systems in fashion design. However, this proliferation has also introduced significant network security vulnerabilities. This paper presents a discussion on establishing an effective network intrusion detection system tailored to the unique aspects of mobile networks, aiming to safeguard the security and reliability of VR applications in clothing design. We propose a deep learning-based intrusion detection algorithm that leverages the features of wireless networks and mobile applications to monitor and analyze traffic data in real time. Training and validation datasets are utilized to assess the model's detection performance across various scenarios. Experimental findings indicate that the proposed intrusion detection system can proficiently identify multiple types of network attacks, achieving a high detection rate coupled with a low false positive rate. The system demonstrates strong real-time performance and accuracy, allowing it to adapt to the dynamic nature of mobile network environments. The mobile network-based intrusion detection system holds significant application potential in the realm of VR fashion design, providing a secure and dependable platform for designers.