Details
| Original language | English |
|---|---|
| Title of host publication | 2007 IEEE Congress on Evolutionary Computation, CEC 2007 |
| Pages | 3719-3726 |
| Number of pages | 8 |
| Publication status | Published - 2007 |
| Event | 2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore Duration: 25 Sept 2007 → 28 Sept 2007 |
Publication series
| Name | 2007 IEEE Congress on Evolutionary Computation, CEC 2007 |
|---|
Abstract
A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We concentrate on performance of respective genes; genes are necessary to measure a network's performance from a sensor's viewpoint. We conclude that the choice of genes has a profound influence on the performance of the AIS. We identified a specific MAC layer based gene that showed to be especially useful for detection. We also discuss implementation details of AIS when used with sensor networks.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Software
- Mathematics(all)
- Theoretical Computer Science
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2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 3719-3726 4424955 (2007 IEEE Congress on Evolutionary Computation, CEC 2007).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - AIS for misbehavior detection in wireless sensor networks
T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
AU - Drozda, Martin
AU - Schaust, Sven
AU - Szczerbicka, Helena
PY - 2007
Y1 - 2007
N2 - A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We concentrate on performance of respective genes; genes are necessary to measure a network's performance from a sensor's viewpoint. We conclude that the choice of genes has a profound influence on the performance of the AIS. We identified a specific MAC layer based gene that showed to be especially useful for detection. We also discuss implementation details of AIS when used with sensor networks.
AB - A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We concentrate on performance of respective genes; genes are necessary to measure a network's performance from a sensor's viewpoint. We conclude that the choice of genes has a profound influence on the performance of the AIS. We identified a specific MAC layer based gene that showed to be especially useful for detection. We also discuss implementation details of AIS when used with sensor networks.
UR - http://www.scopus.com/inward/record.url?scp=79955281664&partnerID=8YFLogxK
U2 - 10.1109/CEC.2007.4424955
DO - 10.1109/CEC.2007.4424955
M3 - Conference contribution
AN - SCOPUS:79955281664
SN - 1424413400
SN - 9781424413409
T3 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
SP - 3719
EP - 3726
BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
Y2 - 25 September 2007 through 28 September 2007
ER -