EAGER: USBRCCR: Improving Network Security at the Network Edge

近年来，互联网在我们的日常生活中扮演着越来越重要的角色，家庭网络托管PC、平板电脑、移动的设备沿着更专业的设备，如智能电视、恒温器和其他物联网（IoT）设备。虽然这些设备为用户提供了一系列服务和便利，但它们的代价是将安全漏洞引入家庭网络。因此，用户面临着双重挑战，一方面要保护自己的网络和设备免受可能对商业和公共网站进行分布式拒绝服务攻击的恶意软件（恶意软件）和僵尸网络的攻击，另一方面要保护通过物联网设备传输的越来越多的个人数据流的隐私。本项目从多方面探讨了在这些挑战面前保护家庭网络的问题。具体而言，它包括与巴西互联网服务提供商建立伙伴关系，提供从数千个家庭网络连接获取数据的机会。这允许创建网络行为的基线，以识别由于恶意软件或受损设备而导致的恶意行为。其次，该项目将开发物联网在野外典型使用的行为模型。这将有助于更好地了解敏感和个人信息如何从物联网设备泄漏到物联网提供商。基线和物联网行为模型将导致新的方法来识别异常/恶意行为的存在以及隐私信息的泄露。 在这个项目中进行的研究为社会带来了重大利益。 首先，研究结果将使用户能够增强家庭网络的安全性，更好地保护个人和敏感信息。 其次，该项目将为学生提供大量机会，发展软件和研究技能沿着网络安全技能。该项目解决了现代家庭网络安全的问题。对这一问题的处理办法将是分析性和经验性的。该项目将包括：㈠开发基于统计分析和机器学习的技术，依靠家庭网络中收集的数据来检测和分类恶意网络活动。这些技术将集中在家庭网络内外的恶意活动。(ii)对家庭网络流量进行指纹识别，以检测受损设备并表征此类设备的行为，即使在流被加密时也是如此。(iii)开发工具，帮助用户控制对其数据的访问。

Recent years have seen the Internet playing an increasingly critical role in our daily lives with home networks hosting PCs, tablets, mobile devices along with more specialized devices such as smart televisions, thermostats, and other Internet-of-Things (IoT) devices. While these devices offer users an array of services and conveniences, they come at the cost of introducing security vulnerabilities into the home network. Thus users are confronted with the dual challenges of securing their networks and devices against malicious software (malware) and botnets that may perform distributed denial of service attacks on commercial and public websites and of maintaining the privacy of increasingly personal flows of data through IoT devices.This project takes a multifaceted look at the problem of securing home networks in the face of these challenges. Specifically, it includes a partnership with a Brazilian Internet Service Provider giving access to data from thousands of home network connections. This allows the creation of a baseline of network behavior against which to identify malicious behavior due to malware or compromised devices. Second, the project will develop behavior models of typical use of IoT in the wild. This will allow a better understanding of how sensitive and personal information can leak from IoT devices to IoT providers. The baseline and the IoT behavior models will lead to new methods for identifying the presence of anomalous/malicious behavior as well as leakage of privacy information. The research conducted in this project provides significant benefits to society. First, the results will allow users to enhance the security of their home networks and better protect personal and sensitive information. Second, the project will provide substantial opportunities for students to develop software and research skills along with cybersecurity skills.This project tackles the problem of securing modern home networks. The approach to this problem will be analytical and empirical. The project will consist of:(i) Development of techniques based on statistical analysis and machine learning that rely on data gathered in home networks to detect and classify malicious network activities. These techniques will focus on malicious activities both within and outside home networks.(ii) Fingerprinting of home network traffic to enable detection of compromised devices and characterization of the behavior of such devices even when flows are encrypted. (iii) Development of tools that will help users control access to their data.