Estimating economic losses from cyber-attacks on shipping ports: An optimization-based approach
Estimating economic losses from cyber-attacks on shipping ports: An optimization-based approach
复制标题
估算航运港口网络攻击造成的经济损失：基于优化的方法
DOI：
10.1016/j.trc.2021.103423
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Van Moer, Mark
中科院分区：
文献类型：
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作者：
Weaver, Gabriel A.;Feddersen, Brett;Marla, Lavanya;Wei, Dan;Rose, Adam;Van Moer, Mark
海上运输系统 (MTS) 占全球商品贸易量的 80% 以上，约占美国总产出的六分之一。鉴于国家和全球经济依赖于高效的供应链，港口利益相关者必须制定安全计划以应对所有自然和人为灾害。鉴于最近影响航运港口的网络攻击，以及数十亿美元的网络保险缺口，港口需要了解通过投资自动化和先进物流技术来提高竞争力和提高风险之间的权衡。本文解决了了解影响航运港口运营的网络攻击的经济影响的需要，从而实现风险评估，全面评估港口信息技术 (IT) 和运营技术 (OT) 系统之间的相互作用。我们使用近正交拉丁超立方体 (NOLH) 实验设计，根据实际网络攻击构建交通中断概况，指定 IT/OT 依赖对利益相关者交通资产的运营影响范围。为了捕获物理中断的成本，我们扩展了 Boland 等人的 DynamicDiscretizationDiscovery (DDD) 算法来捕获容量限制并启用延迟建模以适应由于中断而延迟到达的商品。基于支付意愿文献的七种商品类别的经济损失函数用于计算延迟成本，以便利益相关者可以估计中断情况下的经济和运营影响范围。根据佛罗里达州埃弗格莱兹港提供的针对业主港口和码头运营商资产的网络攻击数据得出的结果显示，2017 年 10 月一周内的平均影响分别为 80,000 美元和 120 万美元，2017 年 5 月的影响分别为 141,000 美元和 280 万美元。我们增强的 DDD 算法的运行时性能比现有技术水平提高了一个数量级，并且基于现实世界的端口网络解决了更大的问题。
TheMaritime TransportationSystem (MTS)accounts for more than 80% of global merchandise trade in volume and roughly one-sixth of the Total Gross Output of the United States. Given that national and global economies depend upon efficient supply chains, port stakeholders must develop security plans to respond to all hazards, natural and manmade. Given recent cyber-attacks affecting shipping ports, along with the multi-billion dollar cyber insurance gap, ports need to understand the tradeoffs between increased competitiveness and higher risk through investment in automation and advanced logistics technologies. This article addresses the need to understand the economic impact of cyber-attacks that affect shipping port operations and thereby enable risk assessments that holistically evaluate interactions among portInformation Technology (IT)andOperational Technology (OT)systems. Using aNearly-Orthogonal LatinHypercube(NOLH)experimental design, we construct transportation disruption profiles based on actual cyber-attacks that specify the range of operational effects of IT/OT dependencies on stakeholder transportation assets. To capture the costs of the physical disruption, we extend Boland et al’sDynamicDiscretizationDiscovery (DDD)algorithm to capture capacity constraints and enable delay modeling to accommodate commodities arriving late due to disruption. Economic loss functions for seven commodity categories based on the willingness to pay literature are used to compute delay costs so that stakeholders can estimate the range of economic and operational impacts within a disruption profile. Results based on data for cyber-attacks on landlord port and terminal operator assets provided by Port Everglades, FL illustrate impacts at $80,000 and $1.2M on average during one week in October 2017 and at $141,000 and $2.8M for May 2017 respectively. The runtime performance of our enhanced DDD algorithm improves on the state of the art by an order of magnitude and on larger problem sizes based on real-world port networks.