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Network Inspection for Detecting Strategic Attacks

This article studies a problem of strategic network inspection, in which a defender (agency) is tasked with detecting the presence of multiple attacks in the network. An inspection strategy entails monitoring the network components, possibly in a randomized manner, using a given number of detectors. We formulate the network inspection problem (P) as a large-scale bilevel optimization problem, in which the defender seeks to determine an inspection strategy with minimum number of detectors that ensures a target expected detection rate under worst-case attacks. We show that optimal solutions of (P) can be obtained from the equilibria of a large-scale zero-sum game. Our equilibrium analysis involves both game-theoretic and combinatorial arguments and leads to a computationally tractable approach to solve (P). First, we construct an approximate solution by using solutions of minimum set cover (MSC) and maximum set packing (MSP) problems and evaluate its detection performance. In fact, this construction generalizes some of the known results in network security games. Second, we leverage properties of the optimal detection rate to iteratively refine our MSC/MSP-based solution through a column generation procedure. Computational results on benchmark water networks demonstrate the scalability, performance, and operational feasibility of our approach. The results indicate that utilities can achieve a high level of protection in large-scale networks by strategically positioning a small number of detectors.

Ketersediaan

Informasi Detail

Judul Seri

OPERATIONS RESEARCH; Vol.70 No.2 March-April 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1008-1024

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EQUILIBRIUM ANALYSIS
ATTACK DETECTION
STRATEGIC NETWORK INSPECTION
MULTIPLE RESOURCES
LARGE-SCALE BILEVEL OPTIMIZATION

Info Detail Spesifik

https://doi.org/10.1287/opre.2021.2180

Pernyataan Tanggungjawab

Mathieu Dahan, Lina Sela, Saurabh Amin

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