In recent years, with the rapid development of mobile social networks and services, the research of mobile malicious webpage detection has become a hot topic. Most of the existing malicious webpage detection systems are deployed on desktop systems and servers. Due to the limitation of network transmission delay and computing resources, these existing solutions fail to provide the real-time and lightweight properties for mobile webpage detection. In this paper, we propose an advanced mobile malicious webpage detection framework based on deep learning and edge cloud. Inspired by the idea of edge computing, a multidevice load optimization approach is first introduced to improve detection efficiency. Second, an automatic extraction approach based on deep learning model features is presented to enhance detection accuracy. Furthermore, detection systems can be flexibly deployed on edge nodes and servers, thus providing the properties of resource optimization deployment and real-time detection. Finally, comparative analysis and performance evaluation are presented to show the detection efficiency and accuracy of the proposed framework.

中文翻译：

---

MMWD：基于深度学习和边缘云的高效移动恶意网页检测框架

近年来，随着移动社交网络和服务的快速发展，移动恶意网页检测的研究成为热点。现有的恶意网页检测系统大多部署在桌面系统和服务器上。由于网络传输延迟和计算资源的限制，这些现有的解决方案无法为移动网页检测提供实时和轻量级的特性。在本文中，我们提出了一种基于深度学习和边缘云的高级移动恶意网页检测框架。受边缘计算思想的启发，首先引入了多设备负载优化方法来提高检测效率。其次，提出了一种基于深度学习模型特征的自动提取方法来提高检测精度。此外，检测系统可以灵活部署在边缘节点和服务器上，从而提供资源优化部署和实时检测的特性。最后，通过比较分析和性能评估来展示所提出框架的检测效率和准确性。