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

TL;DR

This paper models and optimizes colluding attacker groups in mobile urban networks using evolutionary algorithms, revealing strategies that significantly degrade network performance in realistic city scenarios.

Contribution

It introduces a novel optimization approach combining network simulation and evolutionary algorithms to identify effective collusion strategies in urban mobile networks.

Findings

Attack patterns greatly reduce network performance.

Evolutionary algorithms exploit specific network weaknesses.

Effective attack strategies vary across different city topologies.

Abstract

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.