Optimization of Real-Valued Self Set for Anomaly Detection Using Gaussian Distribution

摘要

The real-valued negative selection algorithm (RNS) has been a key algorithm of anomaly detection. However, the self set which is used to train detectors has some problems, such as the wrong samples, boundary invasion and the overlapping among the self samples. Due to the fact that the probability of most real-valued self vectors is near to Gaussian distribution, this paper proposes a new method which uses Gaussian distribution theory to optimize the self set before training stage. The method was tested by 2-dimensional synthetic data and real network data. Experimental results show that, the new method effectively solves the problems mentioned before.