Construction and Application Research of Network Intrusion Fraud Detection Model Based on Big Data Analysis

Abstract

Network intrusion and fraud detection are critical components of cybersecurity. The exponential growth in data generated by digital activities necessitates robust and scalable detection mechanisms. This paper explores the construction and application of a network intrusion fraud detection model leveraging big data analysis. By employing machine learning algorithms and real-time data processing techniques, the proposed model aims to detect and mitigate fraudulent activities with high accuracy and efficiency. Case studies and experimental results demonstrate the effectiveness of the model in various network environments. The paper concludes with a discussion on future research directions and challenges in big data-based network intrusion detection.