Satellites have features of high control integration, various working modes, and complex telemetry big data, which make it difficult to evaluate their performance degradation. In this paper, a novel data mining analysis method is proposed to analyze the satellite's telemetry big data, in which sample entropy is calculated to characterize states and support vector data description is utilized to analyze the satellite performance degradation process. The experimental results show that our proposed method could generally describe the performance degradation process of satellites. Meanwhile, it also provides an important approach for the ground-station-monitor to analyze the performance of satellites.is the length of the series, two parameters are defined: m is the embedded dimension of the vector to be formed and r is the threshold that serves as a noise filter. The steps to calculate SamEn are shown as follows:
The new forms of networks labeled IoT are relatively new and which become buzz in this decade. The network architecture lets any smart device loosely connect to the Internet under internet protocol. However, the other dimension of this network facilitates intruders to access the network with no critical efforts. The context of intrusions has been delineated as intrusion practices of other devices connected to an IoT network that are connected to external networks through a gateway. Vice versa, the compromised IoT network intends to communicate with external devices or networks to perform intrusion practices. In this regard, intrusion detection through machine learning demands significant feature selection and optimization techniques. This manuscript endeavored to demonstrate the scope distribution diversity assessment methods of traditional statistical practices toward feature selection and optimization in this regard, the contribution “Distribution Diversity Method of Feature Optimization (DDMFO) to Protect Intrusion Practices on IoT Networks” of this paper uses the Dice Similarity Coefficient procedure to pick the optimum characteristics for the training of the classifier. The classifier that has been adopted in this contribution is Naïve Bayes, trained by the features selected by the proposal. The experimental research concludes the significance of the taxonomy, which demonstrates substantial accuracy and minimal false alarm.
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