Intrusion Detection in Networks by Wasserstein Enabled Many-Objective Evolutionary Algorithms

Andrea Ponti (), Antonio Candelieri, Ilaria Giordani and Francesco Archetti
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Andrea Ponti: Department of Economics, Management and Statistics, University of Milano-Bicocca, 20126 Milan, Italy
Antonio Candelieri: Department of Economics, Management and Statistics, University of Milano-Bicocca, 20126 Milan, Italy
Ilaria Giordani: Department of Computer Science, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
Francesco Archetti: Consorzio Milano Ricerche, 20125 Milan, Italy

Mathematics, 2023, vol. 11, issue 10, 1-14

Abstract: This manuscript explores the problem of deploying sensors in networks to detect intrusions as effectively as possible. In water distribution networks, intrusions can cause a spread of contaminants over the whole network; we are searching for locations for where to install sensors in order to detect intrusion contaminations as early as possible. Monitoring epidemics can also be modelled into this framework. Given a network of interactions between people, we want to identify which “small” set of people to monitor in order to enable early outbreak detection. In the domain of the Web, bloggers publish posts and refer to other bloggers using hyperlinks. Sensors are a set of blogs that catch links to most of the stories that propagate over the blogosphere. In the sensor placement problem, we have to manage a trade-off between different objectives. To solve the resulting multi-objective optimization problem, we use a multi-objective evolutionary algorithm based on the Tchebycheff scalarization (MOEA/D). The key contribution of this paper is to interpret the weight vectors in the scalarization as probability measures. This allows us to use the Wasserstein distance to drive their selection instead of the Euclidean distance. This approach results not only in a new algorithm (MOEA/D/W) with better computational results than standard MOEA/D but also in a new design approach that can be generalized to other evolutionary algorithms.

Keywords: intrusion detection; optimal sensor placement; water distribution network; multi-objective optimization; evolutionary algorithm; Wasserstein distance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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