An Online Side Channel Monitoring Approach for Cyber-Physical Attack Detection of Additive Manufacturing
An Online Side Channel Monitoring Approach for Cyber-Physical Attack Detection of Additive Manufacturing
复制标题
增材制造网络物理攻击检测的在线侧通道监控方法
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Wenmeng Tian
中科院分区：
文献类型：
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作者：
Chenang Liu;Chen Kan;Wenmeng Tian
由于其在制造复杂几何形状方面的突出灵活性，增材制造（AM）在各种使命关键应用（诸如航空航天、医疗保健、军事和运输）中越来越受欢迎。AM制造的逐层方式显着扩大了AM网络物理系统的脆弱性空间，导致机械性能和功能受损的AM部件可能发生变化。此外，基于传统的几何尺寸和公差（GD&T）特征，构建的内部改变非常难以检测。因此，如何实现有效的监控和攻击检测是AM技术更广泛采用的一个非常重要的问题。为了解决这个问题，本文提出了利用边通道进行进程认证。提出了一种基于自动编码器的在线特征提取方法，用于检测网络物理攻击引起的非预期过程/产品变更。监督和无监督的监测方案的基础上实现的提取的功能。为了验证所提出的方法的有效性，两个真实世界的案例研究进行了熔丝制造（FFF）平台上配备了两个加速度计的过程监测。实施了两种不同类型的攻击。结果表明，该方法优于传统的工艺监测方法，并能有效地检测零件的几何形状和层厚变化的真实的时间。
Due to its predominant flexibility in fabricating complex geometries, additive manufacturing (AM) has gain increasing popularity in various mission critical applications, such as aerospace, health care, military, and transportation. The layerby-layer manner of AM fabrication significantly expands the vulnerability space of AM cyber-physical systems, leading to potentially altered AM parts with compromised mechanical properties and functionalities. Moreover, internal alterations of the build are very difficult to detect based on traditional geometric dimensioning and tolerancing (GD&T) features. Therefore, how to achieve effective monitoring and attack detection is a very important problem for broader adoption of AM technology. To address this issue, this paper proposes to utilize side channels for process authentication. An online feature extraction approach is developed based on autoencoder to detect unintended process/product alterations caused by cyber-physical attacks. Both supervised and unsupervised monitoring schemes are implemented based on the extracted features. To validate the effectiveness of the proposed method, two real-world case studies are conducted on a fused filament fabrication (FFF) platform equipped with two accelerometers for process monitoring. Two different types of attacks are implemented. The results demonstrate that the proposed method outperforms conventional process monitoring methods, and can effectively detect part geometry and layer thickness alterations in real time.