ERI: GRAPHSEC: Graph-Based Vehicular Communication Security with Adaptive Embedded Learning

ERI：GRAPHSEC：具有自适应嵌入式学习的基于图的车辆通信安全

• 批准号: 2138253
• 负责人: Riadul Islam
• 金额: $ 20万
• 依托单位: University of Maryland Baltimore County
• 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: 2022-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138253&HistoricalAwards=false
• 关键词: ERI; GRAPHSEC; Graph; Based; Vehicular

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).It was more than three decades ago, at the 1986 Society of Automotive Engineers (SAE) conference in Detroit, that Robert Bosch GmbH (Bosch) officially released the controller area network (CAN) specification, yet still today, this widely used in-vehicle network protocol remains as a critical security concern in modern vehicles. While automotive manufacturers race to introduce new autonomous vehicles, a society eager to deploy autonomous technologies must, with equal interest, pursue complementary efforts in securing underlying communication fabrics and gain insights from exploring existing deployed technologies. Recent studies show that cyber attacks can compromise CAN communication. This is not surprising given that vehicular communication lacks well-defined source and destination addresses within packets on which security policies may have been built and relies on the good behavior of all devices to enable functionality defined by overlaid sequences of many brief messages. These characteristics result in an attacker's single point of entry to monitor messages and broadcast unverifiable information. This research aims to improve intra-vehicular communication security and help designers integrate network properties with the sensors' physical properties to build highly robust, low-cost security systems. The proposed techniques have the potential to improve vehicular network safety and reduce cost. The PI will also conduct several educational and outreach efforts such as: (1) supervise one Ph.D. theses and several undergraduate senior design projects; (2) introduce a new course on intra-vehicular communication and security in the computer engineering curriculum at UMBC; (3) support the underprivileged students, through an REU program, since about 22% of the population in greater Baltimore area lives below the poverty line; (4) since UMBC is a Minority-Serving Institutions, the PI will continue recruiting undergraduate and K-12 researchers through the Center for Women In Technology program and local schools. In this proposal, PI first builds graphs from the CAN messages and proposes two unique approaches to secure CAN communications. The PI will apply graph-theory-based machine learning and statistical algorithms to a CAN bus to improve the reliability of the CAN bus. The intent is to develop security monitors that can be tailored to any CAN-based control system using an FPGA-based, off-the-shelf deployable platform. Notably, the PI proposes to investigate (1) Graph-based low-cost naive Bayes algorithms for vehicular security; (2) A statistical filter (SF) in the input stage of our novel scaled graph-based neural networks (GNN) for a low-cost and adaptive CAN communication; (3) Developing a flexible and configurable CAN protocol Testbed to help researchers develop new physical-property (i.e., voltage, skew, sensors & interconnect aging)-based algorithms, collect data and analyze their CAN systems.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资助（公法117-2）.三十多年前，在底特律举行的1986年汽车工程师协会（SAE）会议上，罗伯特博世有限公司（博世）正式发布了控制器局域网（CAN）规范，然而时至今日，这种广泛使用的车载网络协议仍然是现代车辆中的关键安全问题。虽然汽车制造商竞相推出新的自动驾驶汽车，但渴望部署自动驾驶技术的社会必须以同样的兴趣，在保护底层通信结构方面做出互补努力，并从探索现有部署的技术中获得见解。最近的研究表明，网络攻击可能会危及CAN通信。这并不奇怪，因为车辆通信在可能已经建立了安全策略的分组内缺乏明确定义的源地址和目的地地址，并且依赖于所有设备的良好行为来实现由许多简短消息的叠加序列定义的功能。这些特征导致攻击者的单一入口点监视消息和广播无法验证的信息。本研究旨在提高车内通信的安全性，并帮助设计人员将网络特性与传感器的物理特性相结合，以构建高度鲁棒、低成本的安全系统。所提出的技术有可能提高车辆网络的安全性和降低成本。PI还将开展几项教育和推广工作，例如：（1）监督一名博士。论文和几个本科生高级设计项目;（2）在UMBC的计算机工程课程中引入一门关于车内通信和安全的新课程;（3）通过REU计划支持贫困学生，因为大巴尔的摩地区约22%的人口生活在贫困线以下;（4）由于UMBC是一个少数民族服务机构，PI将继续通过技术计划和当地学校的妇女中心招募本科生和K-12研究人员。 在该提案中，PI首先从CAN消息构建图形，并提出两种独特的方法来保护CAN通信。PI将基于图论的机器学习和统计算法应用于CAN总线，以提高CAN总线的可靠性。其目的是开发安全监视器，可以使用基于FPGA的现成可部署平台为任何基于CAN的控制系统量身定制。值得注意的是，PI建议研究（1）用于车辆安全的基于图的低成本朴素贝叶斯算法;（2）用于低成本和自适应CAN通信的新型缩放基于图的神经网络（GNN）输入阶段的统计滤波器（SF）;（3）开发灵活和可配置的CAN协议测试床，以帮助研究人员开发新的物理属性（即，该奖项反映了NSF的法定使命，并通过使用基金会的智力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

Resonant Compute-In-Memory (rCIM) 10T SRAM Macro for Boolean Logic

• DOI: 10.1109/iccd58817.2023.00026
• 发表时间: 2023-11
• 期刊: 2023 IEEE 41st International Conference on Computer Design (ICCD)
• 作者: Dhandeep Challagundla;Ignatius-In Bezzam;Biprangshu Saha;Riadul Islam

Feasibility Prediction for Rapid IC Design Space Exploration

快速 IC 设计空间探索的可行性预测

• DOI: 10.3390/electronics11071161
• 发表时间: 2022
• 期刊: Electronics
• 影响因子: 2.9
• 作者: Islam, Riadul

Design Automation of Series Resonance Clocking in 14-nm FinFETs

• DOI: 10.1007/s00034-023-02458-4
• 发表时间: 2023-08
• 期刊: Circuits, Systems, and Signal Processing
• 作者: Dhandeep Challagundla;Ignatius-In Bezzam;Riadul Islam

Early Stage DRC Prediction Using Ensemble Machine Learning Algorithms Prédiction de la DRC à un stade précoce à l’aide d’un ensemble d’algorithmes d’apprentissage machine

使用集成机器学习算法进行早期 DRC 预测 Prédiction de la DRC à un stade precoce à l’aide d’un ensemble d’algorithmes d’apprentissage machine

• DOI: 10.1109/icjece.2022.3200075
• 发表时间: 2022
• 期刊: IEEE Canadian Journal of Electrical and Computer Engineering
• 影响因子: 2
• 作者: Islam, Riadul

Exploring High-Level Neural Networks Architectures for Efficient Spiking Neural Networks Implementation

探索高级神经网络架构以实现高效的尖峰神经网络实现

• DOI: 10.1109/icrest57604.2023.10070080
• 发表时间: 2023
• 期刊: Electrical and Signal Processing Techniques (ICREST
• 作者: Islam, Riadul;Majurski, Patrick;Kwon, Jun;Tummala, Sri Ranga
• 通讯作者: Tummala, Sri Ranga