SYSTEM AND METHOD FOR SECURING NETWORKS BASED ON CATEGORICAL FEATURE DISSIMILARITIES

1 . A method for detecting deviations from baseline behavior patterns for categorical features, comprising:

determining a first discrete probability distribution for a categorical variable based on a first set of network activity data including at least one instance of the categorical variable;

determining a second discrete probability distribution for a unique observation based on a second set of network activity data including data representing the unique observation;

comparing the second discrete probability distribution to the first discrete probability distribution by applying a distance function to the first and second discrete probability distributions, wherein an output of the distance function is a scalar value representing a difference between the first and second discrete probability distributions;

determining whether the scalar value is above a threshold;

detecting an anomaly with respect to the categorical variable when the scalar value is above the threshold; and

determining that a behavior with respect to the categorical variable is normal when the scalar value is not above the threshold.

2 . The method of claim 1 , further comprising:

performing at least one mitigation when an anomaly is detected.

3 . The method of claim 1 , wherein determining the first discrete probability distribution further comprises:

determining a time window such that activity with respect to the categorical variable is assumed to be fully observed during the determined time window, wherein a duration of the time window is based on a type of the categorical variable, wherein the first discrete probability distribution function is determined based on a portion of the first set of network activity data corresponding to the determined time window.

4 . The method of claim 1 , wherein determining the first discrete probability distribution further comprises:

determining a sub-population of devices and systems indicated in the first network activity data, wherein the sub-population of devices and systems has a common attribute, wherein the portion of the first set of network activity data corresponding to the determined time window is related to the sub-population of devices.

5 . The method of claim 1 , wherein the scalar value increases as the difference between the first and second discrete probability distributions increases.

6 . The method of claim 1 , wherein the threshold is associated with the categorical variable.

7 . The method of claim 1 , wherein each discrete probability distribution indicates a probability of each of a plurality of potential categories for the categorical variable.

8 . The method of claim 1 , wherein the distance function is any of: a cross-entropy distance function, and a chi-squared statistic function.

9 . The method of claim 1 , wherein the categorical variable is any of: a host, a communication channel, and a port.

10 . A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:

determining a first discrete probability distribution for a categorical variable based on a first set of network activity data including at least one instance of the categorical variable;

determining a second discrete probability distribution for a unique observation based on a second set of network activity data including data representing the unique observation;

comparing the second discrete probability distribution to the first discrete probability distribution by applying a distance function to the first and second discrete probability distributions, wherein an output of the distance function is a scalar value representing a difference between the first and second discrete probability distributions;

determining whether the scalar value is above a threshold;

detecting an anomaly with respect to the categorical variable when the scalar value is above the threshold; and

determining that a behavior with respect to the categorical variable is normal when the scalar value is not above the threshold.

11 . A system for detecting deviations from baseline behavior patterns for categorical features, comprising:

a processing circuitry; and

a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:

determine a first discrete probability distribution for a categorical variable based on a first set of network activity data including at least one instance of the categorical variable;

determine a second discrete probability distribution for a unique observation based on a second set of network activity data including data representing the unique observation;

compare the second discrete probability distribution to the first discrete probability distribution by applying a distance function to the first and second discrete probability distributions, wherein an output of the distance function is a scalar value representing a difference between the first and second discrete probability distributions;

determine whether the scalar value is above a threshold;

detect an anomaly with respect to the categorical variable when the scalar value is above the threshold; and

determine that a behavior with respect to the categorical variable is normal when the scalar value is not above the threshold.

12 . The system of claim 11 , wherein the system is further configured to:

perform at least one mitigation when an anomaly is detected.

13 . The system of claim 11 , wherein the system is further configured to:

determine a time window such that activity with respect to the categorical variable is assumed to be fully observed during the determined time window, wherein a duration of the time window is based on a type of the categorical variable, wherein the first discrete probability distribution function is determined based on a portion of the first set of network activity data corresponding to the determined time window.

14 . The system of claim 11 , wherein the system is further configured to:

determine a sub-population of devices and systems indicated in the first network activity data, wherein the sub-population of devices and systems has a common attribute, wherein the portion of the first set of network activity data corresponding to the determined time window is related to the sub-population of devices.

15 . The system of claim 11 , wherein the scalar value increases as the difference between the first and second discrete probability distributions increases.

16 . The system of claim 11 , wherein the threshold is associated with the categorical variable.

17 . The system of claim 11 , wherein each discrete probability distribution indicates a probability of each of a plurality of potential categories for the categorical variable.

18 . The system of claim 11 , wherein the distance function is any of: a cross-entropy distance function, and a chi-squared statistic function.

19 . The system of claim 11 , wherein the categorical variable is any of: a host, a communication channel, and a port.