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

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
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-10-01 至 2024-09-30

项目摘要

项目成果

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中文摘要
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英文摘要
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.
期刊论文(11)
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科研奖励(0)
会议论文
DOI: 10.1109/tnet.2021.3062766
发表时间: 2021-06
期刊: IEEE/ACM Transactions on Networking
影响因子: --
作者: [Trupil Limbasiya;Debasis Das;Sajal K. Das]
通讯作者: 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
DOI: --
发表时间: 2022
期刊: IEEE International Conference on Communications (ICC
影响因子: --
作者: [V. P. K. Madhavarapu, P. Roy]
通讯作者: 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
DOI: --
发表时间: 2021
期刊: ACM Asia Conference on Computer and Communications Security
影响因子: --
作者: [S. Bhattacharjee, V. P.]
通讯作者: 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
影响因子: --
作者: [Roy, Prithwiraj, Bhattacharjee, Shameek, Das, Sajal.]
通讯作者: Das, Sajal.
7
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    Research on Quantum Field Theory without a Lagrangian Description
    • 批准号:
      24ZR1403900
    • 项目类别:
      省市级项目
    • 资助金额:
      --
    • 批准年份:
      2024
    • 负责人:
      SATOSHI NAWATA
    • 依托单位:
    Cell Research
    Cell Research
    Cell Research (细胞研究)