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

网络物理增材层制造，例如3D打印，已经成为一种有前途的技术，通过减少设计师和制造商之间的差距，提供成本，时间和空间有效的解决方案。然而，在支持大规模创新设计和快速原型制作的能力的同时，对知识产权保护的关注也在增加。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。这个探索性研究（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（包括声学、温度、功率和其他）来检测恶意软件和假冒产品。该项目是一个EAGER项目，因为观察到的信号签名的唯一性，以及它们在保护制造过程中的利用，都是高风险的，在挫败攻击方面有可能获得高回报。本项目将证明，在增材层制造系统的生命周期中，可以通过各种非侵入性技术，通过物理领域制造过程中发生的攻击，恢复/重建网络领域中包含的知识产权信息。然后，它将专注于创建机器依赖和机器独立的防御机制，以避免此类攻击。该项目将显著影响美国在技术型制造业方面的竞争力。攻击模型将为3D打印机制造商和CAD工具设计师提供反馈，以建立针对这些新型攻击的防御。此外，它将对爆炸式增长的创客和众包社区在保护其知识产权方面产生重大的社会影响。此外，该项目的方法可用于其他制造系统，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的方法，以防止发生在物理领域的攻击，以获取网络领域的信息。该项目有三个具体目标：1)它将通过展示一种新的攻击模型来证明概念，该模型使用机器学习，信号处理和模式识别技术的组合构建，利用在制造过程中获得的侧信道信息（功率，温度，声学，电磁发射）。2)开发针对3D打印机攻击模型的机器专用防御机制。将演示添加额外物理过程加密的新技术，例如向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的打印过程。3)它将为独立于机器的CAD工具创建一种新的具有安全意识的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，随机生成层描述语言（如G-code），这样对于同一个3D物体，攻击者会向3D打印机发送不同的指令，最终会提取出不同的物理特征。

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.