OAC Core: MedKnights - Towards Secure and Flexible Medical IoT (IoMT) Infrastructure using Generative Adversarial Networks

OAC 核心：MedKnights - 使用生成对抗网络实现安全灵活的医疗物联网 (IoMT) 基础设施

• 批准号: 2212424
• 负责人: Tamer Nadeem
• 金额: $ 59.99万
• 依托单位: Virginia Commonwealth University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212424&HistoricalAwards=false
• 关键词: OAC; Core; MedKnights; Towards; Secure

With growing healthcare demands, the Internet of Medical Things (IoMT) has grown significantly in recent years and is dominating the healthcare industry. However, these smarter and advanced medical devices are very complicated in terms of software and hardware, with defects and vulnerabilities that are found regularly and they are also vulnerable to possible malicious attacks. Healthcare organizations are the new focus of attackers for carrying out IoMT-focused cyberattacks, which are becoming more common. In recent years, ransomware and distributed denial of service (DDoS) attacks are malware-based popular attacks on IoMT devices. Cyberattacks and disruptions in clinical care can have a catastrophic effect on patient safety, which trickles down to the medical staff’s responsiveness. Moreover, because different medical devices have varying vital capabilities, it is critical to enable differentiated network services for these devices with varying critical levels of operation under varying network dynamics. Hence it is crucial to efficiently detect and identify any malicious network activities to eliminate or minimize the impact of these attacks, as well as to detect and identify network traffic belonging to different medical devices.The MedKnights project will extend knowledge in networking, machine learning, and the digital forensic domains in developing a holistic framework that can be deployed at the network edge components and effectively support different fine-grained security services for IoMT networks and devices. The main objectives of the project include: (1) creating a novel medical device testbed, IoMT network traffic datasets, IoMT device behavior signature datasets, and attack datasets; (2) employing generative adversarial networks (GANs) to identify many of the interesting features for medical networks and devices, to perform multi-class classification, automatic feature extraction, and be robust to noise, support drifts in networks, and support continual learning for new devices for securing IoMT networks and devices; and (3) enabling remote memory forensic capabilities for IoMT devices. The project will also carry out a number of educational activities involving K-12, undergraduate, and graduate students, make strong outreach efforts for recruiting and mentoring students from underrepresented groups, as well as enrich undergraduate and graduate curricula through exposing students to cutting-edge research in networking, security, and machine learning. In addition, multiple industrial collaborations are expected within the scope of the project.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.

随着医疗需求的增长，医疗物联网（IoMT）近年来发展迅速，并主导着医疗行业。然而，这些更智能和先进的医疗设备在软件和硬件方面都非常复杂，经常发现缺陷和漏洞，并且它们也容易受到可能的恶意攻击。医疗保健组织是攻击者进行以IoMT为中心的网络攻击的新焦点，这种攻击变得越来越普遍。勒索软件和分布式拒绝服务（DDoS）攻击是近年来流行的基于恶意软件的IoMT设备攻击。网络攻击和临床护理中断可能对患者安全产生灾难性影响，这会影响到医务人员的反应能力。此外，由于不同的医疗设备具有不同的重要功能，因此在不同的网络动态下为这些具有不同关键操作级别的设备启用差异化网络服务至关重要。因此，有效地检测和识别任何恶意网络活动以消除或最大限度地减少这些攻击的影响，以及检测和识别属于不同医疗设备的网络流量至关重要。MedKnights项目将扩展网络，机器学习，和数字取证领域开发一个整体框架，可以部署在网络边缘组件，并有效地支持不同的为IoMT网络和设备提供细粒度的安全服务。该项目的主要目标包括：（1）创建新型医疗设备测试床，IoMT网络流量数据集，IoMT设备行为特征数据集和攻击数据集;（2）采用生成对抗网络（GAN）来识别医疗网络和设备的许多有趣特征，执行多类分类，自动特征提取，对噪声具有鲁棒性，支持网络中的漂移，并支持对新设备的持续学习，以保护IoMT网络和设备;以及（3）实现IoMT设备的远程存储器取证能力。该项目还将开展一系列涉及K-12，本科生和研究生的教育活动，为招募和指导来自代表性不足群体的学生做出强有力的宣传努力，并通过让学生接触网络，安全和机器学习方面的前沿研究来丰富本科生和研究生课程。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

MirageNet - Towards a GAN-based Framework for Synthetic Network Traffic Generation

MirageNet - 迈向基于 GAN 的合成网络流量生成框架

• 发表时间: 2022
• 期刊: IEEE Global Communications Conference 2022
• 作者: Nukavarapu, Santosh K.;Ayyat, M.;Nadeem, T.
• 通讯作者: Nadeem, T.

Dynamic Deep Neural Network Adversarial Attacks for Edge-based IoT Devices

针对基于边缘的物联网设备的动态深度神经网络对抗攻击

• 发表时间: 2022
• 期刊: IEEE Global Communications Conference 2022
• 作者: Ayyat, M.;Nukavarapu, Santosh K.;Nadeem, T.
• 通讯作者: Nadeem, T.