Non-harmful insertion of data mimicking computer network attacks

1. A computer-implemented method, comprising:

determining based on historical data, by a computing system, which anomalous real network connections to generate between a plurality of computing systems in a network to create the anomalous real network connections based on stochastic models, the anomalous real network connections forming a k-path between the plurality of computing systems comprising a plurality of directed edges, each directed edge comprising associated data;

generating, by the plurality of computing systems, the determined anomalous real network connections between the plurality of computing systems in the network and data mimicking an attack; and

transmitting, by the plurality of computing systems, the generated data mimicking the attack between the plurality of computing systems using the determined anomalous real network connections forming respective k-paths, wherein

the k-path is a subgraph of size k that has diameter k, and

k is at least two.

2. The computer-implemented method of claim 1 , wherein the generated data comprises Domain Name Server (“DNS”) traffic that is transmitted in a path comprising computing systems that have never communicated before.

3. The computer-implemented method of claim 1 , wherein the data mimicking the attack is designed to mimic one or more specific types of attacks based on known types of real attacks.

4. The computer-implemented method of claim 1 , further comprising:

building stochastic models, by the computing system, for each directed edge in k-paths of a plurality of the anomalous network connections; and

comparing historical parameters with current parameters in a time window, by the computing system, to determine a level of anomalousness of the k-paths of the plurality of the anomalous network connections.

5. The computer-implemented method of claim 4 , wherein the historical data comprises historical connection patterns between the plurality of computing systems.

6. The computer-implemented method of claim 1 , further comprising:

collecting, by the computing system, the generated data mimicking the attack;

analyzing, by the computing system, the collected data;

determining, by the computing system, whether an attack has occurred based on the collected data; and

verifying, by the computing system, that the attack was correctly detected.

7. The computer-implemented method of claim 6 , further comprising:

providing output of results, by the computing system, indicating effectiveness of the verification.

8. A computer program embodied on a non-transitory computer-readable medium, the computer program configured to cause at least one processor to:

determine, based on historical data, which network connections to generate that form a k-path between a plurality of computing systems to create an anomaly by applying stochastic models for each edge in the k-path;

generate the determined network connections in the k-path and data mimicking an attack to create the anomaly; and

transmit the generated data mimicking the attack between the plurality of computing systems using the determined anomalous real network connections forming the k-path, wherein

the k-path is a subgraph of size k that has diameter k, and

k is at least two.

9. The computer program of claim 8 , the program further configured to cause the at least one processor to:

verify that a data collection mechanism for the network actually measured data pertaining to the generated connections; and

provide output of results indicating a degree of success or failure of the verification.

10. The computer program of claim 8 , wherein the network anomaly is designed to mimic one or more specific types of attacks based on known types of real attacks.

11. The computer program of claim 8 , wherein the generated data comprises Domain Name Server (“DNS”) traffic that is transmitted in a path comprising computing systems that have never communicated before.

12. The computer program of claim 8 , the program further configured to cause the at least one processor to:

build stochastic models for each directed edge in the k-paths of a plurality of the determined network connections; and

compare historical parameters with current parameters in a time window to determine a level of anomalousness of the k-paths of the plurality of the determined network connections.

13. The computer program of claim 8 , wherein the historical data comprises historical connection patterns between the plurality of computing systems.

14. A computer-implemented method, comprising:

inserting, by a plurality of computing systems, traffic in a k-path in a network comprising computing systems that do not normally communicate, the traffic deviating from stochastic models for each edge of the k-path;

detecting the inserted k-path as anomalous, by a computing system, based on analysis of the inserted traffic;

verifying, by the computing system, that an attack was correctly detected based on the detected inserted k-path; and

providing output of results, by the computing system, indicating a degree of success or failure of the verification, wherein

the k-path comprises a plurality of directed edges, each directed edge comprising associated data,

the k-path is a subgraph of size k that has diameter k, and

k is at least two.

15. The computer-implemented method of claim 14 , wherein the inserted traffic is designed to mimic one or more specific types of attacks based on known types of real attacks.

16. The computer-implemented method of claim 14 , further comprising:

building stochastic models, by the computing system, for each directed edge in the k-path; and

comparing historical parameters with current parameters in a time window, by the computing system, to determine a level of anomalousness of the k-path.

17. The computer-implemented method of claim 14 , further comprising:

determining based on historical data, by the computing system, which traffic to insert between the computing systems to create an anomaly, wherein

the historical data comprises historical connection patterns between the plurality of computing systems.

18. A computer program embodied on a non-transitory computer-readable medium, the computer program configured to cause at least one processor to:

determine, based on historical data, which network connections to generate that form a k-path between a plurality of computing systems to create an anomaly by applying stochastic models for each edge in the k-path;

generate the determined network connections in the k-path to create the anomaly;

verify that a data collection mechanism for the network actually measured data pertaining to the generated connections; and

provide output of results indicating a degree of success or failure of the verification, wherein

the k-path is a subgraph of size k that has diameter k, and

k is at least two.

19. The computer program of claim 18 , wherein the network anomaly is designed to mimic one or more specific types of attacks based on known types of real attacks.

20. The computer program of claim 18 , wherein the generated data comprises Domain Name Server (“DNS”) traffic that is transmitted in a path comprising computing systems that have never communicated before.

21. The computer program of claim 18 , the program further configured to cause the at least one processor to:

build stochastic models for each directed edge in the k-paths of a plurality of the determined network connections; and

compare historical parameters with current parameters in a time window to determine a level of anomalousness of the k-paths of the plurality of the determined network connections.

22. The computer program of claim 18 , wherein the historical data comprises historical connection patterns between the plurality of computing systems.

23. A computer program embodied on a non-transitory computer-readable medium, the computer program configured to cause at least one processor to:

determine, based on historical data, which network connections to generate that form a k-path between a plurality of computing systems to create an anomaly by applying stochastic models for each edge in the k-path;

build stochastic models for each directed edge in the k-paths of a plurality of the determined network connections;

compare historical parameters with current parameters in a time window to determine a level of anomalousness of the k-paths of the plurality of the determined network connections; and

generate the determined network connections in the k-path to create the anomaly, wherein

the k-path is a subgraph of size k that has diameter k, and

k is at least two.

24. The computer program of claim 23 , the program further configured to cause the at least one processor to:

verify that a data collection mechanism for the network actually measured data pertaining to the generated connections; and

provide output of results indicating a degree of success or failure of the verification.

25. The computer program of claim 23 , wherein the network anomaly is designed to mimic one or more specific types of attacks based on known types of real attacks.

26. The computer program of claim 23 , wherein the generated data comprises Domain Name Server (“DNS”) traffic that is transmitted in a path comprising computing systems that have never communicated before.

27. The computer program of claim 23 , wherein the historical data comprises historical connection patterns between the plurality of computing systems.

28. A computer-implemented method, comprising:

inserting, by a plurality of computing systems, traffic in a k-path in a network comprising computing systems that do not normally communicate, the traffic deviating from stochastic models for each edge of the k-path;

building stochastic models, by the computing system, for each directed edge in the k-path; and

comparing historical parameters with current parameters in a time window, by the computing system, to determine a level of anomalousness of the k-path; and

detecting the inserted k-path as anomalous, by a computing system, based on analysis of the inserted traffic, wherein

the k-path comprises a plurality of directed edges, each directed edge comprising associated data,

the k-path is a subgraph of size k that has diameter k, and

k is at least two.

29. The computer-implemented method of claim 28 , further comprising:

verifying, by the computing system, that an attack was correctly detected based on the detected inserted k-path; and

providing output of results, by the computing system, indicating a degree of success or failure of the verification.

30. The computer-implemented method of claim 28 , wherein the inserted traffic is designed to mimic one or more specific types of attacks based on known types of real attacks.

31. The computer-implemented method of claim 28 , further comprising:

determining based on historical data, by the computing system, which traffic to insert between the computing systems to create an anomaly, wherein

the historical data comprises historical connection patterns between the plurality of computing systems.

32. A computer-implemented method, comprising:

determining based on historical data, by the computing system, which traffic to insert between the computing systems to create an anomaly;

inserting, by a plurality of computing systems, traffic in a k-path in a network comprising computing systems that do not normally communicate, the traffic deviating from stochastic models for each edge of the k-path;

detecting the inserted k-path as anomalous, by a computing system, based on analysis of the inserted traffic, wherein

the k-path comprises a plurality of directed edges, each directed edge comprising associated data,

the k-path is a subgraph of size k that has diameter k,

k is at least two, and

the historical data comprises historical connection patterns between the plurality of computing systems.

33. The computer-implemented method of claim 32 , further comprising:

verifying, by the computing system, that an attack was correctly detected based on the detected inserted k-path; and

providing output of results, by the computing system, indicating a degree of success or failure of the verification.

34. The computer-implemented method of claim 32 , wherein the inserted traffic is designed to mimic one or more specific types of attacks based on known types of real attacks.

35. The computer-implemented method of claim 32 , further comprising:

building stochastic models, by the computing system, for each directed edge in the k-path; and

comparing historical parameters with current parameters in a time window, by the computing system, to determine a level of anomalousness of the k-path.