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| rdf:type
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| Description
| - Owing to the distributed nature of denial-of-service attacks, it is tremendously challenging to detect such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a game theoretic method is introduced, namely cooperative Game-based Fuzzy Q-learning (G-FQL). G-FQL adopts a combination of both the game theoretic approach and the fuzzy Q-learning algorithm in WSNs. It is a three-player strategy game consisting of sink nodes, a base station, and an attacker. The game performs at any time a victim node in the network receives a flooding packet as a DDoS attack beyond a specific alarm event threshold in WSN. The proposed model implements cooperative defense counter-attack scenarios for the sink node and the base station to operate as rational decision-maker players through a game theory strategy. In order to evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using NS-2 simulator. The model is subsequently compared against other existing soft computing methods, such as fuzzy logic controller, Q-learning, and fuzzy Q-learning, in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed models attack detection and defense accuracy yield a greater improvement than existing above-mentioned machine learning methods. In contrast to the Markovian game theoretic, the proposed model operates better in terms of successful defense rate. 2014 Elsevier Ltd.
- Owing to the distributed nature of denial-of-service attacks, it is tremendously challenging to detect such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a game theoretic method is introduced, namely cooperative Game-based Fuzzy Q-learning (G-FQL). G-FQL adopts a combination of both the game theoretic approach and the fuzzy Q-learning algorithm in WSNs. It is a three-player strategy game consisting of sink nodes, a base station, and an attacker. The game performs at any time a victim node in the network receives a flooding packet as a DDoS attack beyond a specific alarm event threshold in WSN. The proposed model implements cooperative defense counter-attack scenarios for the sink node and the base station to operate as rational decision-maker players through a game theory strategy. In order to evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using NS-2 simulator. The model is subsequently compared against other existing soft computing methods, such as fuzzy logic controller, Q-learning, and fuzzy Q-learning, in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed models attack detection and defense accuracy yield a greater improvement than existing above-mentioned machine learning methods. In contrast to the Markovian game theoretic, the proposed model operates better in terms of successful defense rate. 2014 Elsevier Ltd. (en)
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| Title
| - Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks
- Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks (en)
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| skos:prefLabel
| - Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks
- Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks (en)
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| skos:notation
| - RIV/61989100:27240/14:86092820!RIV15-MSM-27240___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...iv/cisloPeriodika
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/61989100:27240/14:86092820
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - WSN; Security; Intrusion detection and prevention systems; Game theory; Fuzzy Q-learning; Cooperative game IDPS (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...odStatuVydavatele
| - GB - Spojené království Velké Británie a Severního Irska
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/nazevZdroje
| - ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...UplatneniVysledku
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| http://linked.open...v/svazekPeriodika
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| http://linked.open...iv/tvurceVysledku
| - Abraham Padath, Ajith
- Anuar, N. B.
- Kiah, M.L.M.
- Patel, A.
- Shamshirband, S.
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| http://linked.open...ain/vavai/riv/wos
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| issn
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| number of pages
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| http://bibframe.org/vocab/doi
| - 10.1016/j.engappai.2014.02.001
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| http://localhost/t...ganizacniJednotka
| |
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