EAGER: Cybermanufacturing: Defending Side Channel Attacks in Cyber-Physical Additive Layer Manufacturing Systems

EAGER：网络制造：防御网络物理增材层制造系统中的侧通道攻击

• 批准号: 1546993
• 负责人: Mohammad Al Faruque
• 金额: $ 20万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546993&HistoricalAwards=false
• 关键词: EAGER; Cybermanufacturing; Defending; Side; Channel

Cyber-physical additive layer manufacturing, e.g., 3D printing, has become a promising technology for providing cost, time, and space effective solution by reducing the gap between designers and manufacturers. However, the concern for the protection of intellectual property is arising in conjunction with the capabilities of supporting massive innovative designs and rapid prototyping. Intellectual property in the additive layer manufacturing system consists of: i) geometric design of an object; ii) attributes of an object; iii) process information; and iv) machine information. This EArly-concept Grant for Exploratory Research (EAGER) project seeks to develop defense mechanisms for detecting malware and counterfeit articles using a variety of signals that are observed during the manufacturing process including acoustic, temperature, power, and others. The project is an EAGER because both the uniqueness of the observed signal signatures, and their utilization in securing the manufacturing process are high risk with potential for high reward in thwarting attacks.This project will demonstrate that during the life-cycle of the additive layer manufacturing system, the intellectual property information contained in the cyber domain can be recovered/reconstructed through attacks occurring during the manufacturing process in the physical domain through various non-intrusive techniques. It will then focus on creating both machine-dependent and machine-independent defense mechanisms for avoiding such an attack. This project will significantly impact US competitiveness over technology-oriented manufacturing. The attack model will provide feedback to 3D printer manufacturers and CAD tool designers to build defenses against these new types of attack. Moreover, it will have a significant societal impact to the explosively growing maker and crowd-sourcing community in protecting their intellectual property. In addition, the project's approach can be used in other manufacturing systems, e.g., CNC machines, manufacturing robots, etc. This is possibly the very first approach to create defense for additive layer manufacturing mechanisms against such attacks occurring in the physical domain to get access to information of the cyber domain. This project has three specific objectives: 1) It will demonstrate a proof of concept by presenting a novel attack model constructed using a combination of machine learning, signal processing, and pattern recognition techniques that utilize the side-channel information (power, temperature, acoustic, electromagnetic emission) obtained during the manufacturing process. 2) It will develop a machine-specific defense mechanism against the attack model for the 3D printer. New techniques to add additional physical process encryption, e.g. adding extra information to the G-code to obfuscate the printing process from the attack model between the G-code and the physical manufacturing process, will be demonstrated. 3) It will create a new security-aware 3D-printing algorithm for the machine-independent CAD tools that can protect against such side channel attacks. The 3D-printing algorithm will slice the STL and generate layer description language (e.g. G-code) randomly so that for the same 3D object, different instructions will be sent to the 3D printer and eventually different physical features will be extracted by the attackers.

网络物理增材层制造（例如 3D 打印）已成为一项有前途的技术，可通过缩小设计师和制造商之间的差距来提供成本、时间和空间有效的解决方案。然而，对知识产权保护的担忧与支持大规模创新设计和快速原型制作的能力一起出现。增材层制造系统中的知识产权包括： i) 物体的几何设计； ii) 对象的属性； iii) 过程信息； iv) 机器信息。这个早期概念探索性研究资助 (EAGER) 项目旨在开发防御机制，利用在制造过程中观察到的各种信号（包括声音、温度、功率等）来检测恶意软件和假冒物品。 该项目是一个急切的项目，因为所观察到的信号特征的独特性以及它们在确保制造过程安全中的利用都具有高风险，并且在阻止攻击方面具有潜在的高回报。该项目将证明，在增材层制造的生命周期中系统中，网络域中包含的知识产权信息可以通过各种非侵入性技术，通过物理域制造过程中发生的攻击来恢复/重建。然后，它将专注于创建依赖于机器和独立于机器的防御机制，以避免此类攻击。该项目将极大地影响美国在技术导向型制造业方面的竞争力。该攻击模型将为 3D 打印机制造商和 CAD 工具设计者提供反馈，以构建针对这些新型攻击的防御措施。此外，它将对爆炸性增长的创客和众包社区在保护其知识产权方面产生重大社会影响。此外，该项目的方法还可用于其他制造系统，例如数控机床、制造机器人等。这可能是第一个为增材层制造机制创建防御措施的方法，以抵御物理域中发生的此类攻击以获得访问权限网络领域的信息。 该项目有三个具体目标：1) 它将通过提出一种新颖的攻击模型来演示概念验证，该模型结合了机器学习、信号处理和模式识别技术，利用了侧信道信息（功率、温度、声学信息）。 ，电磁发射）在制造过程中获得。 2）针对3D打印机的攻击模型，开发针对机器特定的防御机制。添加额外物理过程加密的新技术，例如将演示向 G 代码添加额外信息，以混淆 G 代码和物理制造过程之间的攻击模型中的打印过程。 3) 它将为独立于机器的 CAD 工具创建一种新的安全感知 3D 打印算法，可以防止此类侧通道攻击。 3D打印算法会对STL进行切片并随机生成层描述语言（例如G代码），这样对于同一个3D对象，不同的指令将发送到3D打印机，最终攻击者将提取不同的物理特征。