Information technology (IT) infrastructure relies on a globalized supply chain that is vulnerable to numerous risks from adversarial attacks. It is important to protect IT infrastructure from these dynamic, persistent risks by delaying adversarial exploits. In this paper, we propose max‐min interdiction models for critical infrastructure protection that prioritizes cost‐effective security mitigations to maximally delay adversarial attacks. We consider attacks originating from multiple adversaries, each of which aims to find a “critical path” through the attack surface to complete the corresponding attack as soon as possible. Decision‐makers can deploy mitigations to delay attack exploits, however, mitigation effectiveness is sometimes uncertain. We propose a stochastic model variant to address this uncertainty by incorporating random delay times. The proposed models can be reformulated as a nested max‐max problem using dualization. We propose a Lagrangian heuristic approach that decomposes the max‐max problem into a number of smaller subproblems, and updates upper and lower bounds to the original problem via subgradient optimization. We evaluate the perfect information solution value as an alternative method for updating the upper bound. Computational results demonstrate that the Lagrangian heuristic identifies near‐optimal solutions efficiently, which outperforms a general purpose mixed‐integer programming solver on medium and large instances.
Timely Decision Analysis Enabled by Efficient Social Media Modeling
Many decision problems are set in changing environments. For example, determining
the optimal investment in cyber maintenance depends on whether there is evidence
of an unusual vulnerability, such as “Heartbleed,” that is causing an especially high
rate of incidents. This gives rise to the need for timely information to update decision models
so that optimal policies can be generated for each decision period. Social media provide
a streaming source of relevant information, but that information needs to be efficiently
transformed into numbers to enable the needed updates. This article explores the use of
social media as an observation source for timely decision making. To efficiently generate
the observations for Bayesian updates, we propose a novel computational method to fit an
existing clustering model. The proposed method is called k-means latent Dirichlet allocation
(KLDA).We illustrate the method using a cybersecurity problem. Many organizations
ignore “medium” vulnerabilities identified during periodic scans. Decision makers must
choose whether staff should be required to address these vulnerabilities during periods of
elevated risk. Also, we study four text corpora with 100 replications and show that KLDA
is associated with significantly reduced computational times and more consistent model
accuracy.
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- Award ID(s):
- 1409214
- NSF-PAR ID:
- 10058430
- Date Published:
- Journal Name:
- Decision analysis
- Volume:
- 14
- Issue:
- 4
- ISSN:
- 1545-8490
- Page Range / eLocation ID:
- 250-260
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1287/deca.2017.0360
