An MDP Approach for Defending Against Fraud Attack in Cognitive Radio Networks
| Autor: | Hadi Shahriar Shahhoseini, Amir Jafari, Khadijeh Afhamisisi |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Engineering
business.industry Jamming computer.software_genre Computer security Collision Computer Science Applications Theoretical Computer Science Radio networks Sensing data Cognitive radio Decision making methods Reinforcement learning Data mining Markov decision process Electrical and Electronic Engineering business computer |
| Zdroj: | IETE Journal of Research. 61:492-499 |
| ISSN: | 0974-780X 0377-2063 |
| DOI: | 10.1080/03772063.2015.1023749 |
| Popis: | Cognitive radio networks (CRNs) have been introduced in recent years to solve frequency leakage. In these networks, several challenges such as Lion and jamming attacks, giving false sensing information, low accuracy in detection of primary users, and sensing error can lead to decrease in CRN's performance. Selecting appropriate free bands by secondary users (SUs) can prevent interference and increase frequency use. In this paper, we present a decision making method based on reinforcement learning to find the optimal strategy for SU in best channel selection. In our model, effect of collision fraud and sensing error is considered simultaneously. In the proposed model, to overcome falsification of sensing reports, the abstract of spectrum sensing data falsification attack is extracted and modelled by using model-based reinforcement learning method. We formalize different states of system, actions of SUs, and probability transitions between states in the framework of Markov decision process (MDP). Th... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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