Collaborative Research: SaTC: CORE: Small: TAURUS: Towards a Unified Robust and Secure Data Driven Approach for Attack Detection in Smart Living

协作研究：SaTC：核心：小型：TAURUS：迈向智能生活中攻击检测的统一稳健且安全的数据驱动方法

• 批准号: 2030624
• 负责人: Sajal Das
• 金额: $ 25.72万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030624&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

The “smart living” vision aims to improve human quality of life. Cornerstone cyber-physical systems (CPS) like smart homes, smart grid, smart transportation, or smart healthcare generate voluminous amount of time series data through sensor-actuator devices, the so-called Internet of Things (IoT). Such data may be a target of low-profile stealthy attacks hiding behind high randomness of benign smart living IoT data trends, thereby thwarting the accuracy of analytics dependent operations of various applications. The intertwined dependence on data analytics, potential civilian impact of wrong decisions and competitive economic motivations (e.g., by nation adversaries) make the underlying IoT and CPS domains extremely vulnerable to data integrity and availability attacks as addressed in the innovative TAURUS project. This collaborative project will create a tremendous impact by developing a new science of security for emerging IoT-based applications in smart living. It addresses stealthy attacks from both cyber and physical exploits hiding behind high randomness due to human behavioral differences and codifies a unified model at community scale under various attack types and strengths. Thus, the TAURUS project will drastically reduce the number of concurrently running security solutions and corresponding cross coordination to secure IoT applications. Additionally, the project will recruit and mentor undergraduate and graduate students, including women and underrepresented minority students, as well as train K-12 students through various schemes at partner institutions.The novelty of TAURUS project lies in the unified, lightweight, data-driven approaches towards security analytics across IoT domains in smart living. The invariant-based unified anomaly or intrusion detection theory is unique as it captures both linear and non-linear relationships in data from multiple IoT devices. It also ensures sharp deviations under various attacks yet remaining undisturbed under no attacks, while hiding differences in data skewness, symmetry, dynamics, and configuration across different IoT domains. The proposed response mechanism will gather evidence on the presence, type, severity, and strategies of threats. Finally, based on biological information theoretic concepts extracted from the evidence, the TAURUS project will develop a novel unified trust framework to identify compromised IoT devices with high accuracy under stealthy attacks. Validation of the developed solutions will use real datasets from smart meters and phasor measurement units in smart grid, and vehicle-to-vehicle and vehicle-to-infrastructure data in smart transportation. The proposed security framework is potentially applicable to other smart living IoT context such as smart homes and water distribution networks. A dedicated website will maintain codes, simulation and real-world datasets for two years beyond the project period. Instructions on data cleaning, preparation, and transformations will be available in a GitHub repository. Research findings and results will be disseminated via TAURUS website and publications in peer-reviewed high quality conferences and journals.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.

“智慧生活”愿景旨在提高人类生活质量。智能家居、智能电网、智能交通或智能医疗等基础网络物理系统（CPS）通过传感器-执行器设备（所谓的物联网（IoT））生成大量的时间序列数据。这些数据可能是隐藏在良性智能生活物联网数据趋势的高随机性背后的低调隐形攻击的目标，从而阻碍各种应用程序的分析依赖操作的准确性。对数据分析的相互依赖，错误决策的潜在平民影响和竞争性经济动机（例如，国家对手）使底层物联网和CPS域极易受到数据完整性和可用性攻击，如创新的TAURUS项目所述。这个合作项目将通过为智能生活中新兴的基于物联网的应用开发一种新的安全科学来产生巨大的影响。它解决了由于人类行为差异而隐藏在高随机性背后的网络和物理漏洞的隐形攻击，并在各种攻击类型和强度下在社区规模上编制了统一的模型。因此，TAURUS项目将大大减少同时运行的安全解决方案的数量，以及相应的交叉协调，以确保物联网应用的安全。此外，该项目还将招募和指导本科生和研究生，包括女性和代表性不足的少数民族学生，并通过合作机构的各种计划培训K-12学生。TAURUS项目的新奇在于统一，轻量级，数据驱动的方法，以实现智能生活中物联网领域的安全分析。基于不变量的统一异常或入侵检测理论是独一无二的，因为它可以捕获来自多个物联网设备的数据中的线性和非线性关系。它还确保了在各种攻击下的急剧偏差，但在没有攻击的情况下保持不受干扰，同时隐藏了不同物联网域中数据偏斜、对称、动态和配置的差异。拟议的应对机制将收集有关威胁的存在、类型、严重程度和战略的证据。最后，基于从证据中提取的生物信息理论概念，TAURUS项目将开发一种新的统一信任框架，以在隐形攻击下高精度地识别受损的物联网设备。验证开发的解决方案将使用智能电网中智能电表和相量测量单元的真实的数据集，以及智能交通中的车辆到车辆和车辆到基础设施数据。所提出的安全框架可能适用于其他智能生活物联网环境，如智能家居和供水网络。一个专门的网站将在项目期结束后的两年里维护代码、模拟和真实世界数据集。有关数据清理、准备和转换的说明将在GitHub存储库中提供。研究结果和成果将通过TAURUS网站和同行评审的高质量会议和期刊上的出版物传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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

MComIoV: Secure and Energy-Efficient Message Communication Protocols for Internet of Vehicles

• DOI: 10.1109/tnet.2021.3062766
• 发表时间: 2021-06
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Trupil Limbasiya;Debasis Das;Sajal K. Das

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

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.

Real Time Stream Mining based Attack Detection in Distribution Level PMUs for Smart Grids

智能电网配电级 PMU 中基于实时流挖掘的攻击检测

• DOI: 10.1109/globecom42002.2020.9322072
• 发表时间: 2020
• 期刊: IEEE Conference and Exhibition on Global Telecommunications (IEEE GLOBECOM
• 作者: Roy, Prithwiraj;Bhattacharjee, Shameek;Das, Sajal.
• 通讯作者: Das, Sajal.

A holistic approach to power efficiency in a clock offset based Intrusion Detection Systems for Controller Area Networks

• DOI: 10.1016/j.pmcj.2021.101385
• 发表时间: 2021-06
• 期刊: Pervasive Mob. Comput.
• 作者: Subir Halder;M. Conti;Sajal K. Das