No abstract

Some intelligent detection methods for ultra dense network attacks are likely to generate false alarms in the application process. In order to improve the security in ultra dense network, an intelligent detection method based on edition learning is designed. Considering the SRP change rate, different thresholds are set, the node switching structural features of ltra dense networks are extracted, the function sets that can effectively control error detection are selected, the host recognition algorithm is designed, the function field selection model based on joint learning is constructed, the iteration points are created in the feasible domain, real-time network traffic is collected, and the doattack intelligent detection model is optimized. Experimental results: in the paper, the average probability of non intelligent detection methods for attacks in ultra dense networks is 24.864%, which shows that when combined with federated learning algorithm, it has more advantages in practical performance.

Power big data is the practice of big data concepts, methods, and technologies in the power industry. It is composed of structured data and unstructured data. It mainly involves power generation, transmission, transformation, and distribution. Power big data can reflect the macro-economy in real time, promote economic and social development, and improve the internal management level and economic benefits of the power industry enterprises. The integration of power big data technology and power system simulation calculation can update system analysis in real-time and provide new perspectives and methods for system analysis.