System and method for detecting lateral movement and data exfiltration

1. A system for network management comprising:

one or more devices on a network to:

monitor network data transmitted over the network between a plurality of internal devices on the network and between at least one of the plurality of internal devices and at least one external device outside the network;

detect a first order indicator of compromise on the network based on the network data transmitted over the network;

detect a second order indicator of compromise on the network based on the network data transmitted over the network;

detect one or more indicators of a compromised entity detected by a honey-host;

correlate the one or more indicators of the compromised entity detected by the honey-host, the first order indicator of compromise on the network, and the second order indicator of compromise on the network,

wherein the compromised entity is one of the plurality of internal devices;

generate a risk score for the network data based on correlating the one or more indicators of the compromised entity detected by the honey-host, the first order indicator of compromise on the network, and the second order indicator of compromise on the network; and

generate a preventative action on data associated with the network based on comparing the risk score and a threshold.

2. The system of claim 1 , wherein the one or more devices are further to:

detect the first order indicator of compromise on the network based at least in part on an analysis of east-west network traffic of the network data transmitted over the network; and

detect the second order indicator of compromise on the network based at least in part on an analysis of north-south network traffic of the network data transmitted over the network.

3. The system of claim 1 , wherein the compromised entity includes one or more of:

a digital device,

an application, or

a rogue user on a network.

4. The system of claim 1 , wherein the one or more devices are further to:

collect the network data based on the network data being identified as malicious or malware.

5. The system of claim 1 , wherein the one or more devices are further to:

flag the network data as suspicious.

6. The system of claim 1 , wherein first order indicator of compromise on the network includes one or more of:

an attempt to prove the honey-host for open ports to gain access to the honey-host, or an attempt to examine or move data on a honey-host; and

wherein the second order indicator of compromise includes a behavior pattern of the one or more devices observed from the one or more devices.

7. The system of claim 1 , wherein the one or more devices are further to:

detect real-time observations of an end-user device,

the end-user device being associated with the network;

compare the real-time observations and a behavior profile for the end-user device; and

generate one or more anomalies based on the comparison of the real-time observations and the behavior profile for the end-user device.

8. A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to:

monitor network data transmitted over the network between a plurality of internal devices on the network and between at least one of the plurality of internal devices and at least one external device outside the network;

detect a first order indicator of compromise on the network based on the network data transmitted over the network;

detect a second order indicator of compromise on the network based on the network data transmitted over the network;

correlate one or more indicators of a compromised entity detected by a honey-host, the first order indicator of compromise on the network, and the second order indicator of compromise on the network,

wherein the compromised entity is one of the plurality of internal devices;

generate a risk score for the network data based on correlating the one or more indicators of the compromised entity detected by the honey-host, the first order indicator of compromise on the network, and the second order indicator of compromise on the network; and

generate a preventative action on data associated with the network based on comparing the risk score and a threshold.

9. The non-transitory computer-readable medium of claim 8 , wherein the risk score is generated based on an asset value assigned to the device and a current security posture of the device.

10. The non-transitory computer-readable medium of claim 8 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

detect the first order indicator of compromise on the network based at least in part on an analysis of east-west network traffic of the network data transmitted over the network; and

detect the second order indicator of compromise on the network based at least in part on an analysis of north-south network traffic of the network data transmitted over the network.

11. The non-transitory computer-readable medium of claim 8 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

generate a behavior profile for at least one end-user device under control of a network user.

12. The non-transitory computer-readable medium of claim 8 , wherein the compromised entity includes one or more of:

a digital device,

an application, or

a rogue user on a network.

13. The non-transitory computer-readable medium of claim 8 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

collect the network data based on the network data being identified as malicious or malware.

14. The non-transitory computer-readable medium of claim 8 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

flag the network data as suspicious.

15. A method comprising:

monitoring, by a device, network data transmitted over the network between a plurality of internal devices on the network and between at least one of the plurality of internal devices and at least one external device outside the network;

detecting, by the device, a first order indicator of compromise on the network based on the network data transmitted over the network;

detecting, by the device, a second order indicator of compromise on the network based on the network data transmitted over the network;

detecting, by the device, one or more indicators of a compromised entity detected by a honey-host;

correlating, by the device, the one or more indicators of the compromised entity detected by the honey-host, the first order indicator of compromise on the network, and the second order indicator of compromise on the network,

wherein the compromised entity is one of the plurality of internal devices;

generating, by the device, a risk score for the network data based on correlating the one or more indicators of the compromised entity detected by the honey-host, the first order indicator of compromise on the network, and the second order indicator of compromise on the network; and

generating, by the device, a preventative action on data associated with the network based on the risk score.

16. The method of claim 15 , wherein risk score is generated based on an asset value assigned to the device and a current security posture of the device.

17. The method of claim 15 , further comprising:

detecting the first order indicator of compromise on the network based at least in part on an analysis of east-west network traffic of the network data transmitted over the network; and

detecting the second order indicator of compromise on the network based at least in part on an analysis of north-south network traffic of the network data transmitted over the network.

18. The method of claim 15 , further comprising:

detecting real-time observations of an end-user device,

the end-user device being associated with the network;

comparing the real-time observations and a behavior profile for the end-user device; and

generating one or more anomalies based on the comparison of the real-time observations and the behavior profile for the end-user device.

19. The method of claim 15 , further comprising:

collecting the network data based on the network data being identified as malicious or malware.

20. The method of claim 15 , wherein first order indicator of compromise on the network includes one or more of:

an attempt to prove the honey-host for open ports to gain access to the honey-host, or an attempt to examine or move data on a honey-host; and

wherein the second order indicator of compromise includes one or more of:

a behavior pattern of the one or more devices observed from the one or more devices.