NeTS: Small: RUI: Towards Secure Home Networks

NetS：小型：RUI：迈向安全的家庭网络

• 批准号: 1816995
• 负责人: Kuai Xu
• 金额: $ 38.28万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816995&HistoricalAwards=false
• 关键词: NeTS; Small; RUI; Towards; Secure

The last decade has witnessed the rapid growth of residential broadband connections, home networks, smart home devices, and Internet-of-Things (IoT) systems. However, securing home networks poses a serious challenge, because many home users have little technical expertise to manage, secure and troubleshoot their networks and connected devices. A compelling solution to secure and manage home networks requires an automated and network-based solution that summarizes what is going on in home networks, explains why some observed behaviors are associated with normal or suspicious activities, and suggests how to defend against security threats and stop unexpected behaviors with simple and actionable steps. To improve understanding of the research problems and challenges in securing home networks, this project develops a built-in behavior-oriented traffic profiling system on programmable home routers to collect, analyze and make sense of incoming, outgoing and internal traffic of all Internet-capable devices in home networks.This research project will address the following research problems: i) how to automatically collect traffic data from home networks with little impact on application performance; ii) how to capture and make sense of behavior patterns of home devices via incoming, outgoing, and internal traffic; iii) how to discover network-wide activities across distributed home networks; and iv) how to leverage behavior patterns of home devices to improve security and management.The challenges in uncovering behavior patterns of home devices includes the heterogeneity of home devices, the wide diversity of traffic behavior in different home networks, and the high expectation of user privacy. This project will initiate an educational program on home networks that will engage students in solving the real-world problems. The project brings research and development opportunities for students in underrepresented groups at ASU west campus, a predominantly undergraduate institution where 37% of the applied computing majors are underrepresented minorities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

过去十年见证了住宅宽带连接、家庭网络、智能家居设备和物联网(IoT)系统的快速增长。然而，保护家庭网络是一项严峻的挑战，因为许多家庭用户几乎没有技术专业知识来管理、保护他们的网络和连接的设备并对其进行故障排除。一个引人注目的家庭网络安全和管理解决方案需要一个基于网络的自动化解决方案，该解决方案可以总结家庭网络中正在发生的情况，解释为什么某些观察到的行为与正常或可疑活动相关，并建议如何通过简单可行的步骤防御安全威胁并阻止意外行为。为了更好地理解家庭网络安全方面的研究问题和挑战，本项目在可编程家庭路由器上开发了一个内置的面向行为的流量分析系统，以收集、分析和理解家庭网络中所有具有互联网功能的设备的传入、传出和内部流量。本研究项目将解决以下研究问题：i)如何在不影响应用程序性能的情况下自动收集来自家庭网络的流量数据；ii)如何通过传入、传出和内部流量来捕获和理解家庭设备的行为模式；iii)如何发现跨分布式家庭网络的全网活动；如何利用家庭设备的行为模式来提高安全性和管理性。揭示家庭设备行为模式的挑战包括家庭设备的异构性、不同家庭网络中流量行为的广泛差异以及对用户隐私的高期望。这个项目将启动一个关于家庭网络的教育计划，让学生参与解决现实世界的问题。该项目为亚利桑那州立大学西校区代表性不足群体的学生带来了研究和发展机会。亚利桑那州立大学西校区是一所以本科生为主的机构，37%的应用计算专业是代表性不足的少数民族。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

An End-to-End System for Monitoring IoT Devices in Smart Homes

用于监控智能家居中物联网设备的端到端系统

• DOI: 10.1109/ccnc49033.2022.9700731
• 发表时间: 2022
• 期刊: IEEE Consumer Communications & Networking Conference (CCNC
• 作者: Erkert, Keith;Lamontagne, Andrew;Chen, Jereming;Cummings, John;Hoikka, Mitchell;Xu, Kuai;Wang, Feng
• 通讯作者: Wang, Feng

Characterizing and Mining Traffic Patterns of IoT Devices in Edge Networks

• DOI: 10.1109/tnse.2020.3026961
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Network Science and Engineering
• 影响因子: 6.6
• 作者: Yinxin Wan;Kuai Xu;Feng Wang;G. Xue

IoTMosaic: Inferring User Activities from IoT Network Traffic in Smart Homes

• DOI: 10.1109/infocom48880.2022.9796908
• 发表时间: 2022-05
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
• 作者: Yinxin Wan;Kuai Xu;Feng Wang;G. Xue

An Effective Machine Learning Based Algorithm for Inferring User Activities From IoT Device Events

• DOI: 10.1109/jsac.2022.3191123
• 发表时间: 2022-09
• 期刊: IEEE Journal on Selected Areas in Communications
• 影响因子: 16.4
• 作者: G. Xue;Yinxin Wan;Xuanli Lin;Kuai Xu;Feng Wang

IoTAthena: Unveiling IoT Device Activities From Network Traffic

• DOI: 10.1109/twc.2021.3098608
• 发表时间: 2021-05
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Yinxin Wan;Kuai Xu;Feng Wang;G. Xue