Non-harmful insertion of data mimicking computer network attacks

Patent metadata

Field	Value
Publication number	US-2020028864-A1
Application number	US-201816168956-A
Country	US
Kind code	A1
Filing date	Oct 24, 2018
Priority date	Mar 22, 2012
Publication date	Jan 23, 2020
Grant date	—

Abstract

Non-harmful data mimicking computer network attacks may be inserted in a computer network. Anomalous real network connections may be generated between a plurality of computing systems in the network. Data mimicking an attack may also be generated. The generated data may be transmitted between the plurality of computing systems using the real network connections and measured to determine whether an attack is detected.

First claim

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

3 . 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.

4 . 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.

5 . 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.

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

7 . 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.

8 . The computer-implemented method of claim 7 , further comprising:
providing output of results, by the computing system, indicating effectiveness of the verification.

9 . 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; and send data mimicking an attack between the plurality of computing systems using the generated network connections, wherein the k-path is a subgraph of size k that has diameter k, and k is at least two.

10 . The computer program of claim 9 , 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.

11 . The computer program of claim 9 , wherein the network anomaly is designed to mimic one or more specific types of attacks based on known types of real attacks.

12 . The computer program of claim 9 , wherein the generated data comprises Domain Name Server (“DNS”) traffic that is transmitted in a path comprising computing systems that have never communicated before.

13 . The computer program of claim 9 , 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.

14 . The computer program of claim 9 , the program further configured to cause the at least one processor to:
determine, based on historical data, which anomalous real network connections to generate between the plurality of computing systems to create the anomaly.

15 . The computer program of claim 14 , wherein the historical data comprises historical connection patterns between the plurality of computing systems.

16 . 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; 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.

17 . The computer-implemented method of claim 16 , 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.

18 . The computer-implemented method of claim 16 , wherein the inserted traffic is designed to mimic one or more specific types of attacks based on known types of real attacks.

19 . The computer-implemented method of claim 16 , 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.

20 . The computer-implemented method of claim 16 , 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.

Assignees

• Triad Nat Security Llc

Inventors

• Neil Joshua
• Kent Alexander
• Hash Jr Curtis
• Fisk Michael
• Brugh Alexander
• Storlie Curtis
• Uphoff Benjamin

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N5/045

  Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence · CPC title

• H04L63/1433

  Vulnerability analysis · CPC title

• H04L63/1425 Primary

  Traffic logging, e.g. anomaly detection · CPC title

• H04L63/1416

  Event detection, e.g. attack signature detection · CPC title