Interface Detector Based on Vaccination Strategy for Anomaly Detection

Abstract EN

Interface detector is an enhanced negative selection algorithm with online adaptive learning under small training samples for anomaly detection.

It has better detection performance when it has an appropriate self-radius.

Otherwise, overfitting or underfitting would occur.

In the present paper, an improved interface detector, which is based on vaccination strategy, is proposed.

During the testing stage, negative vaccine can overcome overfitting to improve the detection rate and positive vaccine can overcome underfitting to reduce the false alarm rate.

The experimental results show that under the same dataset, self-radius, and training samples condition, the detection rate of the interface detector with negative vaccine is much higher than that of interface detector, SVM, and BP neural network.

Moreover, the false alarm rate of the interface detector with positive vaccine is much lower than that of the interface detector and PSA.