Estimating Economic Losses from Cyber-Attacks on Shipping Ports: An Optimization-Based Approach

The Maritime Transportation System (MTS) accounts for more than 80% of global merchandise trade in volume and roughly a 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 increased 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 port Information Technology (IT) and Operational Technology (OT) systems. We extend Boland et al's Dynamic Discretization Discovery (DDD) algorithm to include capacity constraints and delay arcs to accommodate commodities arriving late due to disruption. Using a Nearly-Orthogonal Latin Hypercube (NOLH) experimental design, we construct disruption profiles based on actual cyber attacks that specify the range of operational effects of IT/OT dependencies on stakeholder transportation assets. 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 provided by Port Everglades, FL, illustrate impacts at $80,000 and $1.2M on average for cyberattacks on landlord port and terminal operator assets during the first week in October, 2017. The runtime performance of our 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.