A Spectrum Feature-Based Security Situation Awareness Algorithm for Network Coordinate System

Network Coordinate System is vulnerable while facing malicious attacks. Traditional works can mitigate the negative influence caused by malicious attacks but the computing processes take too many computation resources. Thus the security awareness mechanism of NCS is taken into consideration. Through which nodes can know whether it is suffering an attack or not, and decide how to deal with the situations they faced on. To identify the current security situation, this paper explores an unsupervised learning method that utilizes the spectral radius of the estimation error matrix of the time series. Our method is evaluated on the Vivaldi algorithm against many attack models. In our simulations, malicious nodes perform all known attack methods against NCS. Experiments show that this method can recognize the situation of the networks swiftly with low computation cost.