How to use the limited attack resources to attack the battlefield communication network is an essential problem in network countermeasure. This paper proposed the concept of weighted separation Ratio (WSR) of the network topology and corresponding optimal attack strategy based on WSR was examined. Firstly, the equivalent weighted topological model of the network is established, and the concept of WSR is proposed to describe the degree of network separation. And then, considering the attack cost, the optimization mathematical model of network attack strategy is developed. Finally, the correctness and effectiveness of the method are verified by theoretical proof and example analysis.
Small sample condition of communication radio signal caused the poorness of individual recognition on radios. To solve this problem, a method about communication radio individual identification based on semi-supervised rectangular network was proposed innovatively. Firstly, the square integral bispectrum feature was extracted from radio signal and then was artificially injected Gaussian noise to be corrupted. The corrupted sample was passed to the encoder of semi-supervised rectangular network for supervised training. The trained parameterization was then mirrored to decoder through the lateral connection across the model. And the output was forced by decoder through unsupervised learning to be closely to the clean input. While the optimal parameters was obtained by minimizing cost function of full network, the essential feature extracted was referred as the individual feature of radio signals. Individual recognition was finally accomplished by a softmax classifier. The robustness of the method proposed was verified on several radio datasets collected in actual environment. And experiment results indicated that the method has superior performance on identifying radio individuals with the same types under small sample condition.
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