物理逻辑炸弹在3D打印机通过新兴的4D技术

Tuan Le, Sriharsha Etigowni, Sizhuang Liang, Xirui Peng, Jerry H Qi, M. Javanmard, S. Zonouz, R. Beyah
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引用次数: 0

摘要

快速原型制造使得增材制造(或3D打印)在航空航天、汽车和医疗等关键应用领域非常有用。这些应用的快速扩展应该促使对3D打印对象的底层安全性进行检查。在本文中,我们介绍了Mystique,这是一种新型的对打印对象的隐形攻击,它利用新兴的4D打印技术的第四维,通过制造过程操纵引入嵌入式逻辑炸弹。魔形术能使视觉上无害的物体在逻辑炸弹启动时表现出恶意行为。它利用制造过程嵌入一个物理逻辑炸弹,可以用特定的刺激触发,以改变打印对象的物理和机械特性。当物体用于无人机、假肢或医疗应用等关键应用时,这些属性的变化可能会导致灾难性的操作故障。我们成功地在几个3D打印案例研究中评估了Mystique,并证明了Mystique可以逃避先前的对策。为了解决这个问题,我们提出了两个缓解策略来防御魔形女。第一种解决方案侧重于在印刷前检测材料的变化,如长丝直径和成分。设计了一种介电传感器电路,用于量化灯丝直径和浓度组成的变化。介质传感器可以检测到0.1mm的丝径变化和10%的浓度组成变化。第二种解决方案试图通过使用成像技术检查打印对象来检测4D攻击。具体而言,我们对打印物体的高分辨率CT图像进行了数据驱动分类。该检测在单个打印层中识别4D攻击的准确率为94.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physical Logic Bombs in 3D Printers via Emerging 4D Techniques
Rapid prototyping makes additive manufacturing (or 3D printing) useful in critical application domains such as aerospace, automotive, and medical. The rapid expansion of these applications should prompt the examination of the underlying security of 3D printed objects. In this paper, we present Mystique, a novel class of stealthy attacks on printed objects that leverage the fourth dimension of emerging 4D printing technology to introduce embedded logic bombs through manufacturing process manipulation. Mystique enables visually benign objects to behave maliciously upon the activation of the logic bomb during operation. It leverages the manufacturing process to embed a physical logic bomb that can be triggered with specific stimuli to change the physical and mechanical properties of the printed objects. These changes in properties can potentially cause catastrophic operational failures when the objects are used in critical applications such as drones, prosthesis, or medical applications. We successfully evaluated Mystique on several 3D printing case studies and showed thatMystique can evade prior countermeasures. To address this, we propose two mitigation strategies to defend against Mystique. The first solution focuses on detecting the change of materials such as filament diameters and composition before printing. A dielectric sensor circuit is designed to quantify filament diameters and concentration composition changes. The dielectric sensor can detect a change of 0.1mm in filament diameters and a change of 10% in concentration composition. The second solution attempts to detect 4D attacks by examining the printed object using imaging techniques. To be specific, we performed data-driven classification on high resolution CT images of printed objects. This detection has an accuracy of 94.6% in identifying 4D attacks in a single printing layer.
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