Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms

Doina Bucur, Giovanni Iacca, Marco Gaudesi, Giovanni Squillero, Alberto Tonda

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)


In novel forms of the Social Internet of Things, any mobile user within communication range may help routing messages for another user in the network. The resulting message delivery rate depends both on the users’ mobility patterns and the message load in the network. This new type of configuration, however, poses new challenges to security, amongst them, assessing the effect that a group of colluding malicious participants can have on the global message delivery rate in such a network is far from trivial. In this work, after modeling such a question as an optimization problem, we are able to find quite interesting results by coupling a network simulator with an evolutionary algorithm. The chosen algorithm is specifically designed to solve problems whose solutions can be decomposed into parts sharing the same structure. We demonstrate the effectiveness of the proposed approach on two medium-sized Delay-Tolerant Networks, realistically simulated in the urban contexts of two cities with very different route topology: Venice and San Francisco. In all experiments, our methodology produces attack patterns that greatly lower network performance with respect to previous studies on the subject, as the evolutionary core is able to exploit the specific weaknesses of each target configuration.
Original languageEnglish
Pages (from-to)416 - 426
Number of pages11
JournalApplied Soft Computing
Publication statusPublished - 2016
Externally publishedYes


  • Cooperative co-evolution, Delay-Tolerant Network, Evolutionary algorithms, Network security, Routing


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