Paper #5

Design of Enterprise Data Security Management Based on IoT and CNN

Fan Gao

Abstract: In the era of rapid digital transformation, enterprise data security faces increasingly complex and dynamic threats. Traditional defense mechanisms are complicated to effectively respond to real-time risks, mainly when enterprises rely extensively on Internet of Things (IoT) devices. To address this problem, this paper proposes and implements a dynamic intelligent security assessment and early warning system based on ResNet-50 architecture and IoT technology. The system builds a distributed IoT data collection platform to collect multi-source data such as network traffic, device status changes, and user behavior in real time. It uses the optimized ResNet-50 model to analyze high-dimensional heterogeneous data streams accurately. The system is deployed in a cloud computing environment and can process large-scale data with low latency. It can instantly detect abnormal activities, conduct threat assessment, and issue alerts based on contextual information. Experimental results show that the system has an accuracy rate of 98.6% for distributed denial of service (DDoS) attacks and 96.2% for malware data leaks, with an average response time of 1.03 seconds, significantly better than traditional detection methods. This study provides an efficient and scalable solution for enterprise data security protection and lays a foundation for further integrating AI-driven models with IoT infrastructure.

Keywords: Convolutional Neural Network; Data Security Detection; Enterprise Data Security; IoT; Residual Network.