论文标题
基于机器学习的5G网络的异常检测
Machine Learning based Anomaly Detection for 5G Networks
论文作者
论文摘要
保护明天的网络将成为一个具有挑战性的领域，这是由于网络安全威胁的增加并扩大了由物联网（IoT）创建的攻击表面，增加网络异质性，增加对虚拟化技术和分布式体系结构的使用增加。本文建议SDS（软件定义的安全性）作为提供自动化，灵活和可扩展网络防御系统的一种手段。 SDS将利用机器学习的最新进展，使用NAS（神经体系结构搜索）设计CNN（卷积神经网络）来检测异常网络流量。 SD可以应用于入侵检测系统，以为5G网络创建更为主动和端到端的防御。为了测试这一假设，已经收集了来自模拟环境的正常和异常网络流，并用CNN进行了分析。该方法的结果很有希望，因为该模型已经确定了良性流量，其准确率和异常流量为96.4％，检测率为96.4％。这证明了网络流分析对各种常见恶意攻击的有效性，还为检测加密恶意网络流量提供了可行的选择。
Protecting the networks of tomorrow is set to be a challenging domain due to increasing cyber security threats and widening attack surfaces created by the Internet of Things (IoT), increased network heterogeneity, increased use of virtualisation technologies and distributed architectures. This paper proposes SDS (Software Defined Security) as a means to provide an automated, flexible and scalable network defence system. SDS will harness current advances in machine learning to design a CNN (Convolutional Neural Network) using NAS (Neural Architecture Search) to detect anomalous network traffic. SDS can be applied to an intrusion detection system to create a more proactive and end-to-end defence for a 5G network. To test this assumption, normal and anomalous network flows from a simulated environment have been collected and analyzed with a CNN. The results from this method are promising as the model has identified benign traffic with a 100% accuracy rate and anomalous traffic with a 96.4% detection rate. This demonstrates the effectiveness of network flow analysis for a variety of common malicious attacks and also provides a viable option for detection of encrypted malicious network traffic.
