HUMIES 2016 ENTRY ================= # 1. The complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result; Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms # 2. The name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); ## Doina Bucur email: d.bucur@rug.nl smail: Nijenborgh 9, 9747 AG Groningen, The Netherlands tel: +31-611733291 ## Giovanni Iacca email: GiovanniIacca@incas3.eu smail: Dr. Nassaulaan 9, 9401 HJ Assen, The Netherlands tel: +39-3898088796 ## Marco Gaudesi email: marco.gaudesi@gmail.com smail: via Goffredo Casalis, 8 / 10143 Torino / ITALY tel: +39-3336556217 ## Giovanni Squillero email: giovanni.squillero@polito.it smail: Politecnico di Torino - DAUIN / Corso Duca degli Abruzzi 24 / 10123 Torino / ITALY tel: +39-0118997091 ## Alberto Tonda email: alberto.tonda@grignon.inra.fr smail: UMR 782 GMPA, INRA / 1 av. Lucien Brétignières / 78850 Thiverval-Grignon / FRANCE tel: +33-130814596 # 3. The name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition); Alberto Tonda # 4. The abstract of the paper(s); 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. # 5. A list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies; (E) The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions. (G) The result solves a problem of indisputable difficulty in its field. # 6. A statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission); (E) Before our paper, to our knowledge, there were only expert evaluations available on the robustness of a Delay-Tolerant Network (DTN), providing loose upper bounds. Our approach showed that the expert evaluations were largely overestimating the reliability of a DTN, and that even a small number of optimized attackers can create considerable disruption, with regards to Data-Delivery Rate and Latency. (G) Assessing the robustness of a DTN is an extremely complex task, probably impossible to perform without simulating the network itself: the nodes of the network move around in scarcely predictable patterns, exchanging messages, with no end-to-end connectivity. # 7. A full citation of the paper (that is, author names; publication date; name of journal, conference, technical report, thesis, book, or book chapter; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); @article{bucur2016optimizing, title={Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms}, author={Bucur, Doina and Iacca, Giovanni and Gaudesi, Marco and Squillero, Giovanni and Tonda, Alberto}, journal={Applied Soft Computing}, volume={40}, pages={416--426}, year={2016}, publisher={Elsevier} } DOI: 10.1016/j.asoc.2015.11.024 # 8. A statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; Any prize money, if any, is to be divided equally among the co-authors. # 9. A statement stating why the authors expect that their entry would be the "best" Delay-Tolerant Networks address the technical issues in heterogeneous network that may lack continuous network connectivity. Examples of such networks are those operating in mobile or extreme terrestrial environments, or planned networks in space. While the adoption of DTNs is ongoing, evaluating exactly *how much* such networks are resilient to disruption is a hard task. Before our contribution, the best estimates available were computed by experts, and basically consisted of loose upper bounds of disruption to working conditions. Our approach tackled this NP-hard problem, evolving group of attackers, and proving that even a relatively small number of malicious nodes in a DTN can seriously impair the network's performance, provided the attackers follow specific paths in a target city. # 10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc. GA - genetic algorithms; the genome is a variable-length set of attackers, each one featuring a movement mode (boat, bus, pedestrian...), a type of disruption (flood nearby nodes with information, or stock and never deliver messages), and a variable-length list of points of interest to visit on the map.