Fast and Accurate Detection of Malicious Users in Cooperative Spectrum Sensing Network
| Autor: | Jinsoo Hwang, Jeankyung Kim, Inje Sung, Ki-Chang Kim, Duckwhan Yoo |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Node (networking) 020206 networking & telecommunications 02 engineering and technology Computer Science Applications Set (abstract data type) Credibility 0202 electrical engineering electronic engineering information engineering Entropy (information theory) 020201 artificial intelligence & image processing Electrical and Electronic Engineering business Time complexity Energy (signal processing) Fusion center Communication channel Computer network |
| Zdroj: | Wireless Personal Communications. 118:1709-1731 |
| ISSN: | 1572-834X 0929-6212 |
| Popis: | Cooperative spectrum sensing allows users to detect available spectrum and utilize it. However, it is known that a few users can easily affect the cooperative decision at the fusion center by reporting falsified sensing data. The problem of detecting malicious users in cooperative spectrum sensing has been studied by numerous researchers. The basic approach is to compute the credibility of the reported data of each user and declare those with low credibility (below some threshold) as malicious. The computation of credibility can be based on several things such as pattern of historic behavior, entropy of the reported data, signal-to-noise ratio, etc. The credibility of the reported data of some user node can be expressed as an attack probability of that node. A node with higher attack probability will have lower credibility while a node with lower attack probability a higher credibility. Then, the problem of computing credibility of a node becomes the problem of computing attack probability of a node given a set of reported data. We can enumerate a list of all possible attack probability vectors for a set of nodes and the list of all possible energy level vectors of the channel for the observed time period, and compute which combination of attack probability and channel energy level vector can have the maximum probability to produce the reported channel energy level. However, the search space is quite large and grows exponentially as the number of user nodes and the number of time slots to observe increase. In this paper, we suggest algorithms that reduce the search space considerably and detect malicious users in linear time instead of exponential time. The suggested algorithms have been implemented and show promising results. |
| Databáze: | OpenAIRE |
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