SYSTEMS AND METHODS FOR SECURITY BREACH DETECTION

• US 20090309725A1
• Filed: 10/02/2008
• Published: 12/17/2009
• Est. Priority Date: 10/03/2007
• Status: Active Grant

• Current Assignee: University of Southern California
• Original Assignee: University of Southern California
• Inventors: Yousefi, Ali, Dibazar, Alireza, Berger, Theodore W., Park, Hyung O.
• Granted Patent: US 8,077,036 B2
• Time in Patent Office: Days
• US Class Current: 340/566
• CPC Class Codes: G01V 1/001 Acoustic presence detection
• CPC Class Codes: G08B 13/1663 using seismic sensing means

Abstract

A system for detecting and classifying a security breach may include at least one sensor configured to detect seismic vibration from a source, and to generate an output signal that represents the detected seismic vibration. The system may further include a controller that is configured to extract a feature vector from the output signal of the sensor and to measure one or more likelihoods of the extracted feature vector relative to set {b_i} (i=1, . . . , i_max) of breach classes b_i. The controller may be further configured to classify the detected seismic vibration as a security breach belonging to one of the breach classes b_i, by choosing a breach class within the set {b_i} that has a maximum likelihood.

Claims

13-1. The system of claim 1, further comprising a wireless transmitter configured to transmit, to a command center, one of:

data representative of the detected seismic vibration; and

data representative of the detected security breach.

16. A method of detecting and identifying a security breach, comprising:

detecting seismic vibration from a source, and generating an output signal that represents the detected seismic vibration;

extracting a feature vector from the output signal;

computing one or more likelihoods for the extracted feature vector relative to set {b_i} (i=1, . . . , i_max) of breach classes b_i; and

classifying the source of the seismic vibration as a security breach belonging to one of the breach classes b_i by choosing a breach class within the set {b_i} that has a maximum likelihood.

17. The method of claim 16, wherein the act of extracting the feature vector from the output signal comprises the acts of:

applying a Fourier transform to the output signal so as to convert the output signal from a time domain signal into a frequency domain signal;

applying to the converted signal a plurality of weighting functions at different frequency bands;

calculating energy of each of the bands; and

applying a discrete cosine transform to the weighted frequency signals. based on the computed likelihood, as one of a plurality of security breach events or as background noise.

18. The method of claim 16, wherein the act of computing the one or more likelihoods for the extracted feature vector relative to set {b_i} (i=1, . . . , i_max) of breach classes b_i comprises the acts of:

adopting a GMM (Gaussian Mixture Model) parametric model for each member b_i of the set {b_i}; and

estimating one or more GMM parameters for said parametric model to compute a conditional density P(O|b_i), where P(O|b_i) represents the probability of O given b_i.

19. The method of claim 18, wherein a mathematical formulation of the likelihood P(b_i|O) in the adopted GMM parametric model comprises:

20. The method of claim 19, wherein the act of estimating the GMM parameters C_i, μ

_m and Σ

_m comprises at least one of;

performing a Baum-Welch re-estimation;

using an EM (expectation-maximization) algorithm; and

using an MLE (maximum likelihood estimation) algorithm.

21. The method of claim 16, wherein the act of choosing the breach class within the set {b_i} that has the maximum likelihood comprises the act of performing Viterbi decoding.

22. The method of claim 16, further comprising the act of wirelessly transmitting to a command center one of:

data representative of the detected seismic vibration; and

data representative of the classified security breach.

23. A computer-readable storage medium having stored therein computer-readable instructions for a processing system, wherein said instructions when executed by said processor cause said processing system to:

extracting a feature vector from an output signal of a seismic sensor;

compute one or more likelihoods for the extracted feature vector relative to a set {b_i} (i=1, . . . , i_max) of breach classes b_i; and

choose a breach class within the set {b_i} that has a maximum likelihood.

24. A wireless transmitter, wherein the wireless transmitter is configured to:

receive from one or more geophones vibration data representative of seismic vibration detected by the geophones, and transmit the vibration data to a command center;

receive from a detecting system security breach data representative of a security breach that has been detected by the detecting system by extracting a feature vector from the vibration data detected by the geophones, and computing one or more likelihoods of the extracted feature vector relative to set {b_i} (I=1, . . . , i_max) of breach classes b_i, and choosing a breach class within the set {b_i} that has a maximum likelihood relative to the extracted feature vector; and

transmit the security breach data to the command center.