A new model for Improvement anomaly detection in network by incremental learning machine in Online Evolving Spiking Neural Networks

Intrusion detection is followed with special importance in computer systems research
and is used to help system security managers to detect intrusion and attack. The importance of
anomaly detection is due to the fact that anomalies in data are important information that can be
used in a wide range of application areas. Intrusion detection methods are used in many application
domains and each domain requires a different method. In this research, a method for improving
intrusion detection in computer networks is presented using stream data based on neural network.
OeSNN-UAD network is used to present the proposed method and it has input and output layers
that produce a candidate output neuron for each new data. The input layer of this network contains
GRF and input neurons, which GRFs are used to filter the input data. In the proposed method, the
ELM algorithm is used to improve the learning process of the OeSNN-UAD network, and this
algorithm has improved the communication between the two layers by being placed between the
input and output layers in the OeSNN-UAD network.The simulation of the proposed method was
done in MATLAB software. In the first experiment, the effect of ELM in the proposed method was
investigated based on the criteria of accuracy, readability, F score, BA, MCC on data classification,
and in the second experiment, the effect of the Wsize parameter on the final performance of the
proposed method was investigated, and the optimal results It gave a good result.