研究目的
To extend signalprint-based Sybil detection methods to work without a priori trust in any observer, allowing any participant in an open wireless network to determine which of its one-hop neighbors are non-Sybil.
研究成果
The Mason test eliminates 99.6–100 percent of Sybil identities in office environments, 91 percent in a crowded high-motion cafeteria, and 96 percent in a high-motion open outdoor environment. It accepts 88–97 percent of conforming identities in the office environments, 87 percent in the cafeteria, and 61 percent in the outdoor environment. The vast majority of rejected conforming identities were eliminated due to motion.
研究不足
The Mason test requires several conforming neighbors and has a limit on total identities to detect when conforming nodes are being selectively jammed. It also has high computation time and high false positive rates in some environments.
1:Experimental Design and Method Selection:
The Mason test protocol is designed to collect RSSI observations from one-hop neighbors without requiring trust in any other node or authority. It includes a challenge-response protocol to detect moving attackers.
2:Sample Selection and Data Sources:
The experiments were conducted with HTC Magic Android smartphones in various operating environments, including office, cafeteria, and outdoor settings.
3:List of Experimental Equipment and Materials:
HTC Magic Android smartphones were used as the experimental devices.
4:Experimental Procedures and Operational Workflow:
The protocol involves three phases: identity collection, randomized broadcast request for RSSI observations, and RSSI observations report. Each participant performs Sybil classification individually.
5:Data Analysis Methods:
The performance of the Mason test was evaluated based on sensitivity and specificity metrics derived from confusion matrices.
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