Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security最新文献

Déjà Vu? Challenges and Opportunities for AM Security from an ICS perspective 似曾相识?从ICS的角度看AM安全的挑战和机遇

Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security Pub Date : 2022-11-07 DOI: 10.1145/3560833.3563556

N. Tippenhauer

引用次数: 0

Spooky Manufacturing: Probabilistic Sabotage Attack in Metal AM using Shielding Gas Flow Control 幽灵制造:使用保护气体流量控制的金属AM的概率破坏攻击

Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security Pub Date : 2022-11-07 DOI: 10.1145/3560833.3563565

Theo Zinner, Grant Parker, N. Shamsaei, Wayne E. King, M. Yampolskiy

引用次数: 1

Stealthy Cyber Anomaly Detection On Large Noisy Multi-material 3D Printer Datasets Using Probabilistic Models 基于概率模型的大噪声多材料3D打印机数据集的隐身网络异常检测

Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security Pub Date : 2022-11-07 DOI: 10.1145/3560833.3563564

Srikanth B. Yoginath, Michael D. Iannacone, Varisara Tansakul, A. Passian, Rob Jordan, Joel Asiamah, M. Ericson, G. Long, Joel A. Dawson

{"title":"Stealthy Cyber Anomaly Detection On Large Noisy Multi-material 3D Printer Datasets Using Probabilistic Models","authors":"Srikanth B. Yoginath, Michael D. Iannacone, Varisara Tansakul, A. Passian, Rob Jordan, Joel Asiamah, M. Ericson, G. Long, Joel A. Dawson","doi":"10.1145/3560833.3563564","DOIUrl":"https://doi.org/10.1145/3560833.3563564","url":null,"abstract":"As Additive Layer Manufacturing (ALM) becomes pervasive in industry, its applications in safety critical component manufacturing are being explored and adopted. However, ALM's reliance on embedded computing renders it vulnerable to tampering through cyber-attacks. Sensor instrumentation of ALM devices allows for rigorous process and security monitoring, but also results in a massive volume of noisy data for each run. As such, in-situ, near-real-time anomaly detection is very challenging. The ideal algorithm for this context is simple, computationally efficient, minimizes false positives, and is accurate enough to resolve small deviations. In this paper, we present a probabilistic-model-based approach to address this challenge. To test our approach, we analyze current measurements from a polymer composite 3D printer during emulated tampering attacks. Our results show that our approach can consistently and efficiently locate small changes in the presence of substantial operational noise.","PeriodicalId":417037,"journal":{"name":"Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115978440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-Based Security Analysis in Additive Manufacturing Systems 基于模型的增材制造系统安全性分析

Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security Pub Date : 2022-11-07 DOI: 10.1145/3560833.3563566

Michael Durling, A. Moitra, Kit Siu, B. Meng, John Carbone, Christopher C. Alexander, K. Castillo-Villar, Gabriela F. Cretu-Ciocarlie

{"title":"Model-Based Security Analysis in Additive Manufacturing Systems","authors":"Michael Durling, A. Moitra, Kit Siu, B. Meng, John Carbone, Christopher C. Alexander, K. Castillo-Villar, Gabriela F. Cretu-Ciocarlie","doi":"10.1145/3560833.3563566","DOIUrl":"https://doi.org/10.1145/3560833.3563566","url":null,"abstract":"Additive manufacturing (AM) is expected to revolutionize industrial manufacturing processes by providing access to readily available, lower cost, high-performance parts, including those with complex designs and diverse materials, not attainable in conventional subtractive machining processes. These benefits are distinctly advantageous for low-volume rapid prototyping. They also reduce the build time of complex, safety-critical components that traditionally require assembly. The advanced product capabilities of AM also make these systems high risk for intellectual property theft, service outage attacks, and sabotage through compromised product quality. Concerns about malicious actors restrict business models and deter industry adoption and investment, especially those requiring secrecy around safety-critical components. In this paper, we analyze the threats to additive manufacturing system security using the Verification Evidence and Resilient Design in Anticipation of Cybersecurity Threats (VERDICT) tool, a model-based system engineering (MBSE) tool. First, we introduce a comprehensive set of attributes to characterize MBSE tools together with a survey of MBSE tools that support cyber analysis. Based on these attributes and the available tools, we select the relevant tool (i.e., VERDICT) and apply it to an example additive manufacturing system. The modeling and analysis are intended to show the functionality of the VERDICT tool in a research context. The signals, properties, and requirements enable the user to experiment with and illustrate the functionality of the tool. Finally, the paper introduces a novel approach for modeling the return on investment (ROI) for additive hardware cybersecurity investments that will lead to a cost-analysis integration with the VERDICT tool.","PeriodicalId":417037,"journal":{"name":"Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122958357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security 2022年ACM CCS增材制造(3D打印)安全研讨会论文集

Proceedings of the 2022 ACM CCS Workshop on Additive Manufacturing (3D Printing) Security Pub Date : 1900-01-01 DOI: 10.1145/3560833

引用次数: 0