Evolving Attackers against Wireless Sensor Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8612

@InProceedings{Mrugala:2016:GECCOcomp,

• author = "Kinga Mrugala and Nilufer Tuptuk and Stephen Hailes",
• title = "Evolving Attackers against Wireless Sensor Networks",
• booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation",
• year = "2016",
• editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing",
• pages = "107--108",
• month = "20-24 " # jul,
• organisation = "SIGEVO",
• address = "Denver, USA",
• publisher = "ACM",
• keywords = "genetic algorithms, genetic programming: Poster",
• publisher_address = "New York, NY, USA",
• isbn13 = "978-1-4503-4323-7",
• DOI = "10.1145/2908961.2908974",
• abstract = "Recent technological improvements in wireless communication and electronics have enabled the development of small, low-cost wireless sensor nodes, capable of monitoring everything from human health to the performance of the electricity grid. A natural consequence is a desire to secure systems containing these nodes. Unfortunately, proving that systems are secure is beyond the current state of the art, and testing for security is problematic: test cases often miss attacks that have never previously been seen. In this paper, we use Genetic Programming (GP) to create attacks against Internet of Things devices, to help identify vulnerabilities before systems are attacked for real. To assess the effectiveness of each attacker, we used it against a wireless sensor network (WSN) with publish-subscribe communications, protected by a literature artificial immune intrusion detection system (IDS). The GP attackers succeeded in suppressing significantly more legitimate messages than a hand-coded attacker, whilst decreasing the likelihood of detection. As a consequence, it was possible to tune the IDS, improving its performance. Whilst these results are preliminary, they demonstrate GP holds significant potential for improving the protection of systems with large attack spaces.",
• notes = "Distributed at GECCO-2016.",

}

Genetic Programming entries for Kinga Mrugala Nilufer Tuptuk Stephen Hailes

Citations