To reduce the high false alarm rate of the distributed fiber intrusion monitoring system in outdoor complex environment, this study proposes and demonstrates an intrusion event discrimination method based on integrated time/frequency domain feature extraction. First, a vibration fragment segmentation algorithm based on a self-adaptive amplitude threshold is developed to distinguish the vibrating part. On this basis, the average fragment interval feature is extracted. Next, the vibration fragment with the maximum energy is chosen as the research target, and the length and peak-to-average ratio are extracted in the time domain, whose energy distribution in the frequency domain is calculated according to wavelet packet decomposition and an integrated time/frequency domain feature vector is formed. Finally, one-versus-one support vector machine is used to classify four common intrusion events: footsteps of a passerby, bicycle rolling, knocking on the fence, and cutting of an optical cable. The experimental results show that the proposed method recognizes the abovementioned four common intrusion events with an average accuracy of 98.33%, which is much more accurate than the methods that only extract the time or frequency domain features. Moreover, the proposed method is immune to the optical power variation in light path. Thus, the proposed method is helpful to improve the utility of the system.