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

3D打印等网络物理附加层制造技术通过缩小设计者和制造商之间的差距，已经成为一种有前途的技术，可以提供成本、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随之产生。附加层制造系统中的知识产权包括：i)对象的几何设计；ii)对象的属性；iii)工艺信息；以及iv)机器信息。这个早期概念探索性研究拨款(AGER)项目寻求开发防御机制，使用在制造过程中观察到的各种信号来检测恶意软件和假冒物品，包括声音、温度、功率和其他信号。该项目是一个迫切的项目，因为观察到的信号特征的唯一性及其在安全制造过程中的利用都是高风险的，在挫败攻击方面具有潜在的高回报。该项目将展示在加层制造系统的生命周期中，通过各种非侵入性技术在物理领域通过制造过程中发生的攻击来恢复/重建网络域中包含的知识产权信息。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。该项目将显著影响美国相对于以技术为导向的制造业的竞争力。攻击模型将向3D打印机制造商和CAD工具设计者提供反馈，以建立对这些新型攻击的防御。此外，它将对爆炸性增长的制造商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，该项目的方法可以用于其他制造系统，例如，数控机床、制造机器人等。这可能是为附加层制造机制创建防御的第一种方法，以抵御在物理域中发生的此类攻击，以访问网络域的信息。该项目有三个具体目标：1)它将演示概念验证，提出一种使用机器学习、信号处理和模式识别技术的组合构建的新型攻击模型，该模型利用在制造过程中获得的侧通道信息(功率、温度、声学、电磁发射)。2)针对3D打印机的攻击模型，开发针对机器的防御机制。将演示添加附加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来自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工具创建一种新的安全感知3D打印算法，该算法可以防御此类侧通道攻击。3D打印算法会对STL进行切片，并随机生成层描述语言(如G代码)，从而针对同一3D对象，向3D打印机发送不同的指令，最终由攻击者提取不同的物理特征。

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.