Synthesis of neurocontroller for multirotor unmanned aerial vehicle based on neuroemulator

This paper presents a method of creating a neurocontroller based on a multilayer perceptron for an unmanned aerial vehicle. We show how a neural network can effectively emulate dynamic characteristics of an aerial craft. Another network learns to control the emulator, using backpropagation algorithm to calculate the error in its control signal. A set of parameters is used to analyze the efficiency of the stabilization and the weights of the neurocontroller are adjusted accordingly. It is shown that the system meets stabilization requirements with sufficient number of iterations. Described method can be used to remotely control unmanned aerial vehicles operating in changing environment

Network anomaly detection using artificial neural networks

This paper presents a method of identifying and classifying network anomalies using an artificial neural network for analyzing data gathered via Netflow protocol. Potential anomalies and their properties are described. We propose using a multilayer perceptron, trained with the backpropagation algorithm. We experiment both with datasets acquired from a real ISP monitoring system and with datasets modified to simulate the presence of anomalies; some Netflow records are modified to contain known patterns of several network attacks. We evaluate the viability of the approach by practical experimentation with various anomalies and iteration sizes