Abstract
Wireless sensor networks (WSNs) are often deployed in open and potentially hostile environments. An attacker can easily capture the sensor nodes or replace them with malicious devices that actively manipulate the communication. Several intrusion detection systems (IDSs) have been proposed to detect different kinds of active attacks by sensor nodes themselves. However, the optimization of the IDSs w.r.t. the accuracy and also sensor nodes’ resource consumption is often left unresolved. We use multi-objective evolutionary algorithms to optimize the IDS with respect to three objectives for each specific WSN application and environment. The optimization on two detection techniques aimed at a selective forwarding attack and a delay attack is evaluated. Moreover, we discuss various attacker strategies ranging from an attacker behavior to a deployment of the malicious sensor nodes in the WSN. The robustness of the IDS settings optimized for six different attacker strategies is evaluated.
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- 1.
Pareto front is a set of non-dominated solutions with respect to all objectives. Thus, a network operator can easily choose between a solution A with a better IDS accuracy but higher resource consumption or solution B with a worse IDS accuracy but lower resource consumption. Solution C, that is dominated by A and B in all objectives is dominated and, thus, is not a member of the Pareto front.
- 2.
As shown below, evolution can improve the results farther.
- 3.
Such traffic can be overheard by less (if any) number of neighbors comparing to a sensor node placed closer to the BS receiving packets from several directions.
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Acknowledgements
We would like to thank Ludek Smolik, Lukas Sekanina and colleagues from CRoCS for the discussions and suggestions. This work was supported by the Czech research Project VG20102014031, programme BV II/2—VS. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.
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Stehlik, M., Matyas, V., Stetsko, A. (2017). Attack Detection Using Evolutionary Computation. In: Abraham, A., Falcon, R., Koeppen, M. (eds) Computational Intelligence in Wireless Sensor Networks. Studies in Computational Intelligence, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-319-47715-2_5
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DOI: https://doi.org/10.1007/978-3-319-47715-2_5
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