NeTS: JUNO2: Collaborative Research: STEAM: Secure and Trustworthy Framework for Integrated Energy and Mobility in Smart Connected Communities

NetS：JUNO2：协作研究：STEAM：智能互联社区中集成能源和移动性的安全可信框架

• 批准号: 1818942
• 负责人: Sajal Das
• 金额: $ 24万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1818942&HistoricalAwards=false
• 关键词: NeTS; JUNO2; Collaborative; Research; STEAM

The rapid evolution of data-driven analytics, Internet of things (IoT) and cyber-physical systems (CPS) are fueling a growing set of Smart and Connected Communities (SCC) applications, including for smart transportation and smart energy. However, the deployment of such technological solutions without proper security mechanisms makes them susceptible to data integrity and privacy attacks, as observed in a large number of recent incidents. If not addressed properly, such attacks will not only cripple SCC operations but also influence the extent to which customers are willing to share data. This in turn will make trustworthiness in SCC applications very challenging. To address this, a synergistic team of researchers from the US and Japan, under the JUNO2 program, will collaborate on this project, called STEAM (Secure and Trustworthy framework for integrated Energy and Mobility) to develop a framework to ensure data privacy, data integrity, and trustworthiness in smart and connected communities. The collaboration provides the project with a significant amount of automotive (transportation) data from Japan, and also access to a testbed in Japan. Although the target applications are smart mobility and smart energy (the choice is deliberate to exploit the complementary strengths of Japan and US in these two domains), the proposed techniques and solutions have wide applicability to other domains, such as smart healthcare. The novelty of the STEAM project lies in its integrated approach to handling security and trustworthiness in SCC applications. Specifically, the research team will develop innovative privacy-preserving algorithms and models for anomaly detection, trust and reputation scoring used by application providers for data integrity and information assurance. Towards that goal, they will study trade-offs between security, privacy, trust levels, resources, and performance using two exemplar applications in smart mobility and smart energy exchange in communities. Finally, they will design a modular, secure and trustworthy middleware architecture that implements privacy-preserving algorithms, resource constraints, and trustworthiness of data sources or content and decision-making schemes. The project has access to smart meter data from Texas, California, and Ireland and a large volume of automobile data from Japan. The evaluation plan includes integration of the project's anomaly detection and trustworthy decision-making algorithms into a smart vehicle route planning application and a transactive energy system in a plug-in electric vehicle testbed in Japan.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.

数据驱动的分析，物联网（IoT）和网络物理系统（CPS）的快速演变正在助长越来越多的智能和连接社区（SCC）应用程序（包括用于智能运输和智能能源）。 但是，如最近发生的许多事件中所观察到的那样，没有适当的安全机制的这种技术解决方案使它们容易受到数据完整性和隐私攻击的影响。如果无法正确解决，此类攻击不仅会削弱SCC操作，而且会影响客户愿意共享数据的程度。反过来，这将使SCC应用程序的可信赖性非常具有挑战性。为了解决这个问题，根据JUNO2计划，来自美国和日本的研究人员的协同团队将在该项目中合作，称为Steam（综合能源和移动性的安全且可信赖的框架），以开发一个框架，以确保在智能和互联社区中的数据隐私，数据完整性，数据完整性以及可信赖性。该协作为该项目提供了来自日本的大量汽车（运输）数据，还可以访问日本的测试台。尽管目标应用是智能移动性和智能能源（选择是故意利用日本和我们在这两个领域中的互补优势），但拟议的技术和解决方案对其他领域（例如智能医疗保健）具有广泛的适用性。 Steam项目的新颖性在于其在SCC应用程序中处理安全性和可信赖性的综合方法。具体而言，研究团队将开发创新的隐私算法和模型，用于应用程序提供商用于数据完整性和信息保证的异常检测，信任和声誉评分。为了实现这一目标，他们将使用两个智能移动性和社区智能能源交换中的示例应用程序来研究安全，隐私，信托水平，资源和绩效之间的权衡。最后，他们将设计一个模块化，安全和值得信赖的中间件体系结构，该体系结构实现了隐私保护算法，资源约束以及数据源或内容和决策方案的可信度。该项目可以访问来自德克萨斯州，加利福尼亚州和爱尔兰的智能电表数据，以及来自日本的大量汽车数据。 评估计划包括将项目的异常检测和值得信赖的决策算法整合到智能车辆路线计划应用程序中，并在日本的插电式电动汽车测试中进行交易的能源系统。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力功能和广泛影响的评估来通过评估来评估的。

项目成果

A Diverse Band-Aware Dynamic Spectrum Access Network Architecture for Delay-Tolerant Smart City Applications

• DOI: 10.1109/tnsm.2020.2969086
• 发表时间: 2020-01
• 期刊: IEEE Transactions on Network and Service Management
• 影响因子: 5.3
• 作者: V. Shah;Brian Luciano;S. Silvestri;Shameek Bhattacharjee;Sajal K. Das

Resilience Against Bad Mouthing Attacks in Mobile Crowdsensing Systems via Cyber Deception

移动群体感知系统中通过网络欺骗抵御恶意攻击的能力

• DOI: 10.1109/wowmom51794.2021.00030
• 发表时间: 2021
• 期刊: IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM
• 作者: Roy, Prithwiraj;Bhattacharjee, Shameek;Alsheakh, Hussein;Das, Sajal K.
• 通讯作者: Das, Sajal K.

A Diversity Index based Scoring Framework for Identifying Smart Meters Launching Stealthy Data Falsification Attacks

基于多样性指数的评分框架，用于识别发起隐形数据伪造攻击的智能电表

• 发表时间: 2021
• 期刊: ACM Asia Conference on Computer and Communications Security
• 作者: S. Bhattacharjee, V. P.

Scalable Pythagorean Mean based Incident Detection in Smart Transportation Systems

智能交通系统中基于可扩展毕达哥拉斯均值的事件检测

• DOI: 10.1145/3603381
• 发表时间: 2023
• 期刊: ACM Transactions on Cyber-Physical Systems
• 影响因子: 2.3
• 作者: Islam, Md. Jaminur;Talusan, Jose Paolo;Bhattacharjee, Shameek;Tiausas, Francis;Dubey, Abhishek;Yasumoto, Keiichi;Das, Sajal K.
• 通讯作者: Das, Sajal K.

Active Learning Augmented Folded Gaussian Model for Anomaly Detection in Smart Transportation

用于智能交通异常检测的主动学习增强折叠高斯模型

• 发表时间: 2022
• 期刊: IEEE International Conference on Communications (ICC
• 作者: V. P. K. Madhavarapu, P. Roy