Systems and methods for network security model
A security apparatus for a local network is in communication with an external electronic communication system and a first electronic device. The apparatus includes a memory device configured to store computer-executable instructions, and a processor in operable communication with the memory device. The processor is configured to implement the stored computer-executable instructions to cause the apparatus to determine a complexity score for the first electronic device, establish a behavioral pattern for the first electronic device operating within the local network, calculate a confidence metric for the first electronic device based on the determined complexity score and the established behavioral pattern, and control access of the first electronic device to the external electronic network according to the calculated confidence metric.
1. A security apparatus for a local network, the apparatus being in communication with an external electronic communication system and a first electronic device, comprising:
a memory device configured to store computer-executable instructions; and
a processor in operable communication with the memory device,
wherein the processor is configured to implement the stored computer-executable instructions to cause the apparatus to:
receive a plurality of flows from a first electronic device;
determine a complexity score for the first electronic device based on the plurality of flows, wherein the complexity score for the first electronic device is determined according to a derived device complexity calculation based on a noise to signal ratio;
generate a model of device communication for the first electronic device based on the plurality of flows and the complexity score;
receive one or more additional communications from the first electronic device;
compare the one or more additional communications to the model of device communications; and
control access of the first electronic device to the external electronic network according to the comparison of the model.
2. The apparatus of claim 1 , wherein the processor is further configured to:
receive a second plurality of flows from a second electronic device;
determine a second complexity score for the second electronic device based on the second plurality of flows; and
generate a second model of device communication for the second electronic device based on the plurality of flows and the complexity score.
3. The apparatus of claim 1 , wherein the processor is further configured to determine if identify the first electronic device based on the complexity score.
4. The apparatus of claim 1 , wherein the processor is further configured to determine the complexity score for the first electronic device based on a number of clusters of destination IP addresses and destination ports.
5. The apparatus of claim 1 , wherein the complexity score is further based on a clustering algorithm with model selection criteria.
6. The apparatus of claim 1 , wherein the processor is further configured to update the model based on an analysis of network traffic of the first electronic device over time.
7. The apparatus of claim 1 , wherein the external electronic communication system is the Internet.
8. The apparatus of claim 1 , wherein the first electronic device is an Internet of Things (IoT) device.
9. The apparatus of claim 1 , wherein the processor is further configured to control access of the first electronic device to the external electronic network by one of routing, limiting, or dropping individual network flows to and/or from the first electronic device.
10. The apparatus of claim 1 , wherein the apparatus comprises a router.
11. The apparatus of claim 10 , wherein the router is in operable communication with a network traffic monitor configured to monitor the individual network flows, and wherein the apparatus receives the one or more additional communications from the network traffic monitor.
12. The apparatus of claim 1 , wherein the model considers a historical record of the individual network flows of the first electronic device on the local network to learn the behavioral pattern of the first device.
13. The apparatus of claim 1 , wherein the processor is further configured to determine that a current network state for the device fits within a discovered boundary of the model.
14. The apparatus of claim 1 , wherein the processor is further configured to monitor a plurality of electronic devices including the first electronic device.
15. The apparatus of claim 1 , wherein the first electronic device executes a first application, and wherein the processor is further configured to:
generate a first model for communications between the first application and the external electronic communication system; and
generate a second model for communications between the first electronic device and the external electronic communication system by removing the first model from the model of device communications.
16. The apparatus of claim 15 , wherein the processor is further configured to use the second model to detect anomalies in communications between the first electronic device and the external electronic communication system.
17. The apparatus of claim 1 , wherein the processor is further configured to allocate resources for the model based on the complexity score.
18. The apparatus of claim 1 , wherein the processor is further configured to measure the first device's complexity based on a signal to noise ratio based on clustered traffic data points in view of unclustered traffic data points.