2010
DOI: 10.1007/978-3-642-11723-7_15
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Abstract: Abstract. Mobile ad hoc networks (MANETs) are a highly promising new form of networking. However they are more vulnerable to attacks than wired networks. In addition, conventional intrusion detection systems (IDS) are ineffective and inefficient for highly dynamic and resourceconstrained environments. Achieving an effective operational MANET requires tradeoffs to be made between functional and non-functional criteria. In this paper we show how Genetic Programming (GP) together with a Multi-Objective Evolutiona… Show more

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Cited by 13 publications
(9 citation statements)
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References 23 publications
(21 reference statements)
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“…However, the performance of the network is degraded in IDAR. In [70], authors used Genetic Programming (GP) along with Multi Objective Evolutionary Algorithm (MOEA) to find out optimal tradeoffs between security criteria and the power consumption of the nodes. This scheme addresses route request flooding and route disruption attacks utilizing anomaly detection as in AODV reactive routing protocol.…”
Section: Intrusion Detection Systemmentioning
confidence: 99%
“…However, the performance of the network is degraded in IDAR. In [70], authors used Genetic Programming (GP) along with Multi Objective Evolutionary Algorithm (MOEA) to find out optimal tradeoffs between security criteria and the power consumption of the nodes. This scheme addresses route request flooding and route disruption attacks utilizing anomaly detection as in AODV reactive routing protocol.…”
Section: Intrusion Detection Systemmentioning
confidence: 99%
“…In , Sen et al . presented first application of evolutionary computation technique in the intrusion detection for MANETs.…”
Section: Intrusion Detection Techniquesmentioning
confidence: 99%
“…presented first application of evolutionary computation technique in the intrusion detection for MANETs. In , they used GP and multi‐objective evolutionary algorithm (MOEA) to present a power‐aware intrusion detection model, where GP is used to select the best features for detection process. MOEA is more effective for searching the best (or near best) tradeoff space.…”
Section: Intrusion Detection Techniquesmentioning
confidence: 99%
“…In our experiments, we use an evolutionary MOO algorithm known as SPEA2 [44] (an optimization of the Strength Pareto Evolutionary Algorithm). SPEA2 is one of the most popular MOO evolutionary algorithms and has been successfully applied in the intrusion detection domain [45,28]. Indeed, it is one of the two MOO algorithms implemented in the ECJ framework [46], which we use in our experiments.…”
Section: Optimization Of the Cost-risk Tradeoffmentioning
confidence: 99%