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The goal of this study is to detect and classify approaching human threats or vehicles, e.g. suicide bombers nearing a secured zone such as military bases. More specifically, this research is focused on (i) developing a vibration recognition system that can detect systematic vibration events; the entity might be a medium, human, animal, or a passenger vehicle, and (ii) discriminating between such a series of events vs. background and a single vibration event, e.g., falling of a tree limb. We have employed a seismic sensor to detect vibrations generated by footsteps and vehicles. A geophone is an inexpensive sensor which provides easy and instant deployment as well as long range detection capability. We have also designed a low power, low noise, and low cost hardware solution to process seismic waves locally where the sensor is located and wireless capability of the system makes it to communicate with a remote command center. Temporal features of the vibration signals were modeled by the dynamic synapse neural network (DSNN) using data recorded in the deserts of Joshua Tree, CA. The system showed 1.7% false recognition rate for the recognition of human footsteps, 6.7% for vehicle, and 0.0% for background. The models were able to reject quadrupedal animal's footsteps (in this study a trained dog). The system rejected dog's footsteps with 0.02% false recognition rate.