ARGUS – An Adaptive Smart Home Security Solution

R.M. Ruwin R. Ratnayake, G.D.N.D.K. Abeysiriwardhena, G. Perera, A. Senarathne, R. Ponnamperuma, B.A. Ganegoda
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引用次数: 1

Abstract

Smart Security Solutions are in high demand with the ever-increasing vulnerabilities within the IT domain. Adjusting to a Work-From-Home (WFH) culture has become mandatory by maintaining required core security principles. Therefore, implementing and maintaining a secure Smart Home System has become even more challenging. ARGUS provides an overall network security coverage for both incoming and outgoing traffic, a firewall and an adaptive bandwidth management system and a sophisticated CCTV surveillance capability. ARGUS is such a system that is implemented into an existing router incorporating cloud and Machine Learning (ML) technology to ensure seamless connectivity across multiple devices, including IoT devices at a low migration cost for the customer. The aggregation of the above features makes ARGUS an ideal solution for existing Smart Home System service providers and users where hardware and infrastructure is also allocated. ARGUS was tested on a small-scale smart home environment with a Raspberry Pi 4 Model B controller. Its intrusion detection system identified an intrusion with 96% accuracy while the physical surveillance system predicts the user with 81% accuracy.
ARGUS -自适应智能家庭安全解决方案
随着IT领域漏洞的不断增加,智能安全解决方案的需求越来越大。通过维护必需的核心安全原则,适应在家工作(WFH)文化已经成为强制性的。因此,实施和维护一个安全的智能家居系统变得更加具有挑战性。ARGUS为输入和输出流量提供全面的网络安全覆盖,防火墙和自适应带宽管理系统以及复杂的闭路电视监控功能。ARGUS就是这样一个系统,它被部署到现有的路由器中,结合了云和机器学习(ML)技术,以确保多个设备之间的无缝连接,包括物联网设备,为客户提供低迁移成本。综合上述功能,ARGUS成为现有智能家居系统服务提供商和用户的理想解决方案,这些服务提供商和用户还需要分配硬件和基础设施。ARGUS在小型智能家居环境中使用树莓派4 B型控制器进行了测试。其入侵检测系统识别入侵的准确率为96%,而物理监控系统预测用户的准确率为81%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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