A novel intelligence and information acquisition system for managing indicators of compromise in distributed responsive manufacturing systems
A novel intelligence and information acquisition system for managing indicators of compromise in distributed responsive manufacturing systems
复制标题
一种新颖的情报和信息获取系统，用于管理分布式响应制造系统中的妥协指标
DOI：
10.1049/icp.2023.2600
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Kumar M
中科院分区：
文献类型：
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作者：
Kumar M
增材制造（AM）是一项革命性的技术，有可能改变制造业并影响数字经济。虽然AM为数字经济带来了机遇，但AM公司必须实施强大的网络安全措施，以保护其设计，软件和设备免受攻击。为了应对这些挑战，本文讨论了一种严格的网络安全措施，以定期评估与AM相关的威胁。本文概述了一种新的情报和信息采集系统（IIAS），识别威胁相关的信息和指标的妥协（IOC）在AM。该系统由三个子系统组成：a）获取与AM相关的威胁信息，重点是AM生态系统中设计文件的完整性违规，B）基于严重性指标对威胁进行量化和优先级排序，以及c）验证和检测Hyperledger Fabric上的数据泄露和交换。此外，IIAS验证与分类账相关的部分的完整性，这有助于检测违规行为并分析AM系统中的各种交互。它还为实现端到端审计跟踪提供了基础，确保在可接受的风险状况内对AM工件进行防篡改跟踪和交付。
Additive manufacturing (AM) is a revolutionary technology that has the potential to transform the manufacturing industry and impact the digital economy. while AM presents opportunities for the digital economy, it is important for AM companies to implement strong cybersecurity measures to protect their designs, software, and equipment from attacks. To address these challenges, this paper discusses a rigorous cyber security measure to regularly assess AM-related threats. This paper outlines a new intelligence and information acquisition system (IIAS) that identifies threat-related information and Indicators of Compromise (IOC) in AM. The system consists of three subsystems: a) acquisition of threat information related to AM with an emphasis on integrity violations of design files in the AM ecosystem, b) quantification and prioritisation of threats based on severity metrics, and c) verifies and detects data breaches and exchanges on Hyperledger Fabric. Furthermore, IIAS verifies the integrity of parts related to a ledger, which helps detect violations and analyse various interactions in AM systems. It also provides the basis for achieving an end-to-end audit trail that ensures tamperproof tracking and delivery of AM artefacts within the acceptable risk profile.