School of Computer Science, Federal University of Uberlândia (UFU), Uberlândia 38400-902, Brazil.
Sensors (Basel). 2022 Apr 9;22(8):2895. doi: 10.3390/s22082895.
The residential environment is constantly evolving technologically. With this evolution, sensors have become intelligent interconnecting home appliances, personal computers, and mobile devices. Despite the benefits of this interaction, these devices are also prone to security threats and vulnerabilities. Ensuring the security of smart homes is challenging due to the heterogeneity of applications and protocols involved in this environment. This work proposes the FamilyGuard architecture to add a new layer of security and simplify management of the home environment by detecting network traffic anomalies. Experiments are carried out to validate the main components of the architecture. An anomaly detection module is also developed by using machine learning through one-class classifiers based on the network flow. The results show that the proposed solution can offer smart home users additional and personalized security features using low-cost devices.
居住环境在技术上不断发展。随着这一发展，传感器已成为智能互联的家用电器、个人电脑和移动设备。尽管这种交互有很多好处，但这些设备也容易受到安全威胁和漏洞的影响。由于涉及到的应用程序和协议的异构性，确保智能家居的安全是一项具有挑战性的任务。本工作提出了 FamilyGuard 架构，通过检测网络流量异常，为家庭环境的安全管理添加新的安全层。通过使用基于网络流量的单类分类器的机器学习，还开发了一个异常检测模块。实验结果表明，该解决方案可以使用低成本设备为智能家居用户提供额外的个性化安全功能。
