Abstract: In the power self incentive networks, the difference property of routing phase group characteristics produces resonance signal,therefore frequency domain migration feature needs to be mined for intrusion signal interception.Traditional methods use shuffled frog leaping algorithm for data mining,and the clustering center vector is close to fuzzy edge,resulting in low search and mining accuracy.An improved mining algorithm of frequency domain migration intrusion feature was proposed based on shuffled frog leaping optimal mode information fusion transfer.The power self combination network system model and mathematical model of intrusion signal are constructed.On the basis of frequency resonant slow fading amplitude equalization principle,the multi-source network attack source signals in the coherent point integrated power accumulation scale coordinate are obtained.The Doppler frequency shift fuzzy search algorithm is used for intrusion signal smoothing processing.The intrusion signal state space modal function of Doppler frequency shift is calculated.Amplitude estimation value is obtained.IIR filtering algorithm is used for signal filtering processing to improve the signal purity.The shuffled frog leaping intrusion detection algorithm based on information fusion of transfer is obtained.Feature mining results are optimized.The frequency domain migration intrusion signal feature mining algorithm is completed.The simulation results show that the algorithm can accurately mine the frequency domain migration feature of intrusion signal.The wave ridge highlight is obvious,and it can improve the detection performance of the intrusion signal in low SNR.

Key words: Shuffled frog leaping algorithm,Frequency domain migration,Data mining,Network