Spoofing is one of the important threats to be faced on GPS receivers. In this attack unknown users transmits fake signals to the GPS receiver on UAS. This type of fake signals may misguides the flight controls to get malfunction. Even sometimes road traffic signals may get changed due these type of malicious user’s fake transmitted signal. For that it needs a high-level security by classifying the signals received. Thus finally the efficiency of network results to provide a high probability of detection and low probability of false alarm through Packet spoofing attack UAV system.
The extracted different parameters from different received GPS signal to develop the input features for the neural network. Next step is to apply specific clustering algorithm for dividing partition and hierarchical clustering which provide dendrogram based clustering results with various partitions. The predictive of random algorithm is the lowest and it has not made use of any data. Item based CF with PCC performs it much better to analyze the impact on the results. Let each cases are implemented certain typical CF algorithm and then it compares their performance in predictive accuracy with metric of MAE. Based on the Packet spoofing attack UAV system
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