ERI: Towards Intelligent, Cost-Efficient, and Adaptive Techniques to Enable Biomedical Hardware-Assisted Cybersecurity

ERI：采用智能、经济高效和自适应的技术来实现生物医学硬件辅助的网络安全

• 批准号: 2139034
• 负责人: Hossein Sayadi
• 金额: $ 19.53万
• 依托单位: California State University-Long Beach Foundation
• 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: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139034&HistoricalAwards=false
• 关键词: ERI; Towards; Intelligent; Cost; Efficient

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).For decades, cybersecurity has been at the forefront of global attention as a serious threat to society, particularly the nation's information technology infrastructure. Medical electronic devices cover a spectrum of equipment implementations, ranging from large diagnostic imaging machines to small mobile devices that patients carry with them. The digitized nature of modern computing platforms used in healthcare systems and their increased connectivity to computer networks have led into the growth of cybersecurity vulnerabilities making such systems a unique target for sophisticated cyber-attacks. The proposed project aims at developing effective and adaptive solutions for emerging healthcare cybersecurity challenges for securing biomedical computing platforms against potential malicious cyber-attacks. In addition, it will result in integrated engineering research projects and educational materials to mentor and train undergraduate and graduate students in the field of Artificial Intelligence (AI) and Machine Learning (ML) for security analysis and biomedical hardware-assisted cybersecurity specially from Under-Represented Minorities (URM). The proposed engineering research and educational activities are planned to provide students from diverse engineering backgrounds such as computer engineering, computer science, and biomedical engineering majors with the necessary knowledge and skills to be competitive in the demanding job market where highly specialized hardware designers, AI/ML engineers, data scientists, and cybersecurity specialist are needed to develop new efficient methods for securing modern computing systems. Traditionally, integrity of data being processed in computing systems has been safeguarded at the software level with the assumption that the underlying hardware is secure against potential attacks. However, biomedical devices have some unique features such as specific security requirements, implementation cost and design trade-offs characteristics, and limited resources and computational power that undermine their security protocols. In addition, strict regulations make it difficult to conduct basic software updates on medical computers and adopting off-the-shelf Anti-Virus (AV) protection is also insufficient for preventing emerging cyber-attacks such as malware. To overcome the performance overhead and inefficiency of conventional software-based solutions, the security in modern biomedical devices should be delegated to the underlying hardware, building a bottom-up solution for securing computing devices rather than treating it as an afterthought. In this project, we will leverage effective AI/ML techniques to develop accurate, low-cost, and adaptive techniques and build a multi-tiered intelligent framework for hardware-assisted cybersecurity in emerging biomedical devices. We will utilize the patterns of low-level hardware features captured by microprocessors’ hardware components in biomedical devices to build novel intelligent techniques for recognizing and classifying emerging cyber-attacks (e.g., malware, side-channel attacks, stealthy attacks, zero-day attacks, etc.) with high accuracy and low computational overheads. The proposed research project targets five major objectives: 1) Comprehensive data collection, benchmarking, and feature analysis of emerging hardware-driven cyber-attacks in biomedical devices’ processors, 2) Developing various standard and advanced machine learning techniques for intelligent hardware-assisted cybersecurity countermeasures using the hardware-related features, 3) Exploring hardware implementation results and on-device trade-off analysis of the intelligent hardware-assisted countermeasures, 4) Developing a reinforcement learning-based decision-maker for adaptive selection of the accurate and cost-efficient detector to facilitate online detection of the cyber-attacks, and 5) Developing a system-level ontology-based cybersecurity analysis framework for an effective automated knowledge reasoning in IoMT devices.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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。几十年来，网络安全一直是全球关注的焦点，对社会构成严重威胁，特别是对国家的信息技术基础设施。医疗电子设备涵盖一系列设备实现，从大型诊断成像机器到患者随身携带的小型移动的设备。医疗保健系统中使用的现代计算平台的数字化性质及其与计算机网络的连接性增加导致网络安全漏洞的增长，使此类系统成为复杂网络攻击的独特目标。该项目旨在为新兴的医疗网络安全挑战开发有效和自适应的解决方案，以保护生物医学计算平台免受潜在的恶意网络攻击。此外，它还将产生综合工程研究项目和教育材料，以指导和培训人工智能（AI）和机器学习（ML）领域的本科生和研究生，用于安全分析和生物医学硬件辅助网络安全，特别是来自代表不足的少数民族（URM）。拟议的工程研究和教育活动计划为来自计算机工程，计算机科学和生物医学工程专业等不同工程背景的学生提供必要的知识和技能，以便在要求严格的就业市场中具有竞争力，高度专业化的硬件设计师，AI/ML工程师，数据科学家，和网络安全专家需要开发新的有效方法来保护现代计算系统。传统上，在计算系统中处理的数据的完整性已经在软件级别得到保护，假设底层硬件是安全的，不受潜在的攻击。然而，生物医学设备有一些独特的功能，如特定的安全要求，实施成本和设计权衡特性，以及有限的资源和计算能力，破坏其安全协议。此外，严格的法规使得难以在医疗计算机上进行基本的软件更新，并且采用现成的反病毒（AV）保护也不足以防止恶意软件等新兴的网络攻击。为了克服传统基于软件的解决方案的性能开销和效率低下，现代生物医学设备的安全性应该委托给底层硬件，构建一个自下而上的解决方案来保护计算设备，而不是将其视为事后的想法。在该项目中，我们将利用有效的人工智能/机器学习技术来开发准确、低成本和自适应的技术，并为新兴生物医学设备中的硬件辅助网络安全构建多层智能框架。我们将利用生物医学设备中微处理器硬件组件捕获的低级硬件特征的模式来构建用于识别和分类新兴网络攻击的新型智能技术（例如，恶意软件、旁道攻击、隐形攻击、零日攻击等）具有高精度和低计算开销。建议的研究项目有五个主要目标：1）对生物医学设备处理器中新兴的硬件驱动的网络攻击进行全面的数据收集、基准测试和特征分析，2）开发各种标准和先进的机器学习技术，用于使用硬件相关功能的智能硬件辅助网络安全对策，3）探索智能硬件辅助对策的硬件实现结果和设备上的权衡分析，4）开发一个基于强化学习的决策器，用于自适应选择准确和成本-有效的检测器，以促进网络攻击的在线检测，5）开发系统级本体-该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

项目成果

Image-Based Zero-Day Malware Detection in IoMT Devices: A Hybrid AI-Enabled Method

IoMT 设备中基于图像的零日恶意软件检测：一种混合 AI 方法

• 发表时间: 2023
• 期刊: 2023 IEEE 24th International Symposium on Quality Electronic Design (ISQED
• 作者: He, Zhangying;Sayadi, Hossein
• 通讯作者: Sayadi, Hossein

Breakthrough to Adaptive and Cost-Aware Hardware-Assisted Zero-Day Malware Detection: A Reinforcement Learning-Based Approach

自适应和成本感知型硬件辅助零日恶意软件检测的突破：基于强化学习的方法

• DOI: 10.1109/iccd56317.2022.00042
• 发表时间: 2022
• 期刊: 2022 IEEE 40th International Conference on Computer Design (ICCD
• 作者: He, Zhangying;Makrani, Hosein Mohammadi;Rafatirad, Setareh;Homayoun, Houman;Sayadi, Hossein
• 通讯作者: Sayadi, Hossein

Towards AI-Enabled Hardware Security: Challenges and Opportunities

• DOI: 10.1109/iolts56730.2022.9897507
• 发表时间: 2022-09
• 期刊: 2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS)
• 作者: H. Sayadi;Mehrdad Aliasgari;Furkan Aydin;S. Potluri;Aydin Aysu;Jacky Edmonds;Sara Tehranipoor