Hybrid Feature Extraction-Based Intrusion Discrimination in Optical Fiber Perimeter Security System

This paper proposes a hybrid feature extraction-based intrusion discrimination scheme for an optical fiber perimeter security system, which concurrently possesses high classification rate and high efficiency. The high classification rate lies in two aspects: On one hand, plentiful contents (including bandwidth segmentation in frequency domain, kurtosis in statistics, and the zero-crossing rate in time domain) are incorporated into the proposed hybrid feature vector; on the other hand, a configurable filter bank is adopted to reduce the intercoupling between features in the hybrid vector. The high efficiency also arises for two reasons: For one thing, the configurable filter bank works in a pipeline stream; for another, an efficient support vector machine is employed to classify hybrid vectors. Experiments demonstrated that the proposed scheme can accurately identify four common intrusions (fence climbing, knocking the cable, waggling, and fence cutting) with an average recognition rate higher than 94%. Moreover, the recognition efficiency is also high.