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Erschienen in:
A Genetic Programming Approach to Cost-Sensitive Control in Wireless Sensor Networks Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Attack Detection Using Evolutionary Computation

verfasst von : Martin Stehlik, Vashek Matyas, Andriy Stetsko

Erschienen in: Computational Intelligence in Wireless Sensor Networks

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Artificial Neural Network Based Real-Time Urban Road Traffic State Estimation Framework
Nächstes Kapitel Computational Intelligence Based Security in Wireless Sensor Networks: Technologies and Design Challenges
Fußnoten
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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Metadaten
Titel
Attack Detection Using Evolutionary Computation
verfasst von
Martin Stehlik
Vashek Matyas
Andriy Stetsko
Copyright-Jahr
2017
Buch
Computational Intelligence in Wireless Sensor Networks

Print ISBN: 978-3-319-47713-8

Electronic ISBN: 978-3-319-47715-2

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-47715-2_5