{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/optimizing-groups-of-colluding-strong","title":"Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms","arxiv_id":"1810.02713","date":"2018-10-05","proceeding":null,"authors":["D. Bucur","G. Iacca","M. Gaudesi","G. Squillero","A. Tonda"],"abstract":"In novel forms of the Social Internet of Things, any mobile user within\ncommunication range may help routing messages for another user in the network.\nThe resulting message delivery rate depends both on the users' mobility\npatterns and the message load in the network. This new type of configuration,\nhowever, poses new challenges to security, amongst them, assessing the effect\nthat a group of colluding malicious participants can have on the global message\ndelivery rate in such a network is far from trivial. In this work, after\nmodeling such a question as an optimization problem, we are able to find quite\ninteresting results by coupling a network simulator with an evolutionary\nalgorithm. The chosen algorithm is specifically designed to solve problems\nwhose solutions can be decomposed into parts sharing the same structure. We\ndemonstrate the effectiveness of the proposed approach on two medium-sized\nDelay-Tolerant Networks, realistically simulated in the urban contexts of two\ncities with very different route topology: Venice and San Francisco. In all\nexperiments, our methodology produces attack patterns that greatly lower\nnetwork performance with respect to previous studies on the subject, as the\nevolutionary core is able to exploit the specific weaknesses of each target\nconfiguration.","url_abs":"http://arxiv.org/abs/1810.02713v1","url_pdf":"http://arxiv.org/pdf/1810.02713v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"optimizing-groups-of-colluding-strong","repo_url":"https://github.com/doinab/DTN-security","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"evolutionary-algorithms","task_name":"Evolutionary Algorithms"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}