Research Initiation Award: Towards Realizing a Self-Protecting Healthcare Information System for the Internet of Medical Things

研究启动奖：实现医疗物联网自我保护医疗信息系统

• 批准号: 1812599
• 负责人: Qian Chen
• 金额: $ 28.02万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-19 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812599&HistoricalAwards=false
• 关键词: Research; Initiation; Award; Towards; Realizing

Research Initiation Awards provide support for faculty at Historically Black Colleges and Universities who are building a research program. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at her home institution, and involves undergraduate students in research experiences. The award to Savannah State University has potential broader and societal impact in a number of areas. The project seeks to develop an autonomic security management framework and apply the framework to realize a self-protecting Healthcare Information System (HIS). The comprehensive self-protection system will enable HISs to autonomously assess their potential security risks, initiate prevention mechanisms, detect realtime intrusions and react to cyber attacks, thus maintaining normal performance and enhancing patient data security and privacy. Undergraduate students will gain research experiences and the research will be integrated in a number of cyber security courses.The research seeks to adopt computing technology to realize a Self-Protecting Healthcare Information System (SPHIS) that can autonomously assess system security risks, initiate prevention mechanisms, detect real-time intrusions and react to cyber attacks with little or no human intervention. Eventually the SPHIS will maintain the Internet of Medical Things (IoMT) ecosystem's normal performance and enhance patients' data security and privacy. An Internet of Things cyber security lab and Healthcare Information System cloud testbed will be built to validate the SPHIS autonomous feature and functions using real-world cyber attacks. The research will determine cyber attacks and use time series forecasting methods to initialize early warning modules and send attack alerts based on the variation of power utilization of IoMT devices. Power consumption of IoMT devices thus will be collected offline when the system is under normal and known cyber attack situations. The power data will be used to set up the IoMT ecosystem's normal behavior region by data mining techniques, and help to identify abnormal behaviors and detect cyber attacks using unsupervised learning techniques. The power data of IoMT devices will be collected online when the HIS is compromised by unknown attacks, and the power data will be analyzed by a network of forensics analysis tools. The attack patterns will be added to update the intrusion detection module. A dynamic intrusion response system will be developed to select the optimal prevention and protection mechanisms for mitigating cyber attacks. The research promises to change the current HIS cyber security state from poorly prepared to well defended against emerging cyber attacks. This research will also contribute to development of the cyber security workforce and enhance security awareness in healthcare organizations.

研究启动奖为正在建立研究计划的历史悠久的黑人学院和大学提供了支持。预计该奖项有助于进一步进一步发展教职员工的研究能力和有效性，改善其家庭机构的研究和教学，并让本科生参与研究经验。萨凡纳州立大学的奖项在许多领域都具有更广泛的社会影响。该项目旨在开发一个自主安全管理框架，并应用该框架以实现自我保护的医疗保健信息系统（HIS）。全面的自我保护系统将使Hiss自主评估其潜在的安全风险，启动预防机制，检测实时入侵并对网络攻击做出反应，从而保持正常性能并增强患者的数据安全性和隐私性。本科生将获得研究经验，该研究将集成到许多网络安全课程中。该研究试图采用计算技术来实现自我保护的医疗保健信息系统（SPHIS），该系统可以自主评估系统安全风险，启动预防机制，检测实时侵入，对网络攻击几乎没有人为介入。最终，SPHIS将维护医学事物（IOMT）生态系统的正常表现，并增强患者的数据安全和隐私。物联网网络安全实验室和医疗保健信息系统将建立云测试床，以使用现实世界的网络攻击来验证SPHIS自主功能和功能。该研究将确定网络攻击和使用时间序列预测方法，以初始化预警模块并根据IOMT设备的功率利用率发送攻击警报。因此，当系统处于正常和已知的网络攻击情况下时，IOMT设备的功耗将离线收集。功率数据将用于通过数据挖掘技术来设置IOMT生态系统的正常行为区域，并有助于使用无监督的学习技术来识别异常行为并检测网络攻击。当IOMT设备的功率数据被未知攻击损害时，将在线收集，并且将通过取证分析工具网络对功率数据进行分析。将添加攻击模式以更新入侵检测模块。 将开发一个动态入侵响应系统，以选择缓解网络攻击的最佳预防和保护机制。该研究有望将其网络安全状况从准备不足的情况转变为防御新兴的网络攻击的情况。这项研究还将有助于网络安全劳动力的发展，并提高医疗保健组织的安全意识。

项目成果

ExHPD: Exploiting Human, Physical and Driving Behaviors to Detect Vehicle Cyber Attacks

ExHPD：利用人类、物理和驾驶行为来检测车辆网络攻击

• DOI: 10.1109/jiot.2021.3069951
• 发表时间: 2021
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Chen, Qian;Romanowich, Paul;Castillo, Jorge;Roy, Krishna Chandra;Chavez, Gustavo;Xu, Shouhuai
• 通讯作者: Xu, Shouhuai

IoTCop: A Blockchain-Based Monitoring Framework for Detection and Isolation of Malicious Devices in Internet-of-Things Systems

• DOI: 10.1109/jiot.2020.3022033
• 发表时间: 2021-03
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Sreenivas Sudarshan Seshadri;D. Rodríguez;Mukunda Subedi;K. Choo;S. Ahmed;Qian Chen;Junghee Lee

DeepRan: Attention-based BiLSTM and CRF for Ransomware Early Detection and Classification

• DOI: 10.1007/s10796-020-10017-4
• 发表时间: 2020-06
• 期刊: Information Systems Frontiers
• 影响因子: 5.9
• 作者: K. Roy;Qian Chen

A Survey of Intrusion Detection Systems Leveraging Host Data

• DOI: 10.1145/3344382
• 发表时间: 2020-01-01
• 期刊: ACM COMPUTING SURVEYS
• 影响因子: 16.6
• 作者: Bridges, Robert A.;Glass-Vanderlan, Tarrah R.;Chen, Qian (Guenevere)
• 通讯作者: Chen, Qian (Guenevere)

State of Science in Alarm System Safety: Implications for Researchers, Vendors, and Clinical Leaders

报警系统安全的科学现状：对研究人员、供应商和临床领导者的影响

• DOI: 10.2345/0899-8205-56.1.19
• 发表时间: 2022
• 期刊: Biomedical Instrumentation & Technology
• 作者: Sowan, Azizeh K.;Staggers, Nancy;Reed, Charles C.;Austin, Tommye;Chen, Qian;Xu, Shouhuai;Lopez, Emme
• 通讯作者: Lopez, Emme