Network security analysis using real-time and batch detection engines

Patent metadata

Field	Value
Publication number	US-9813435-B2
Application number	US-201715415747-A
Country	US
Kind code	B2
Filing date	Jan 25, 2017
Priority date	Aug 31, 2015
Publication date	Nov 7, 2017
Grant date	Nov 7, 2017

Abstract

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

First claim

What is claimed is:

1. A network security breach detection system comprising:
a real-time path including a real-time analysis engine configured to receive first event data indicative of first activity on a computer network, the real-time event analysis engine configured to detect first indicia of possible security breaches in a real-time processing mode based on the first event data, and to generate real-time analysis result data representing the first indicia for output to a user;
a non-volatile storage system to store the real-time analysis result data; and
a batch path including a batch analysis engine configured to operate concurrently with the real-time analysis engine, the batch analysis engine further configured to retrieve, from the non-volatile storage system, the real-time analysis result data and second event data indicative of second activity on the computer network, the first event data and the second event data each including timestamped machine data indicative of performance or operation of a component in an information technology environment, the second event data having been stored in the non-volatile storage system prior to analysis of the first event data by the real-time analysis engine, the batch analysis engine further configured to detect, in a batch mode, second indicia of possible security breaches based on the second event data and the real-time analysis result data.

2. The network security breach detection system as recited in claim 1 , wherein the real-time analysis engine is further configured to use outputs of the batch analysis engine, in conjunction with the first event data, to detect the first indicia of possible security breaches.

3. The network security breach detection system as recited in claim 1 , wherein the first event data is a portion of an unbounded stream of event data.

4. The network security breach detection system as recited in claim 1 , wherein the real-time path further includes a data intake and preparation engine configured to receive the first event data and the second event data from a plurality of heterogeneous data sources in the computer network, and to perform preprocessing of the first event data and the second event data before the first event data and the second event data are provided to the real-time analysis engine and the batch analysis engine, respectively, wherein the preprocessing includes at least one of: parsing the first and second event data, enriching the first and second data, and filtering the first and second event data.

5. The network security breach detection system as recited in claim 1 , wherein the real-time path further includes a message broker to receive the first event data and the second event data and to pass the first event data to the real-time analysis engine and to pass the second event data to the batch analysis engine.

6. The network security breach detection system as recited in claim 1 , wherein the real-time path further includes:
a data intake and preparation engine configured to receive the first event data and the second event data from a plurality of heterogeneous data sources in the computer network, and to perform preprocessing of the first event data and the second event data before the first event data and the second event data are provided to the real-time analysis engine and the batch analysis engine, respectively; and
a message broker to receive the preprocessed first event data and second event data from the data intake and preparation engine and to pass the preprocessed first event data to the real-time analysis engine and to pass the preprocessed second event data to the batch analysis engine.

7. The network security breach detection system as recited in claim 1 , wherein the real-time analysis engine includes a real-time anomaly detection engine and a real-time threat detection engine, wherein some of the first indicia of possible security breaches are detected by the real-time anomaly detection engine as security related anomalies and others of the first indicia are detected by the real-time threat detection engine as security related threats based on the detected anomalies.

8. The network security breach detection system as recited in claim 1 , wherein the real-time analysis engine includes a real-time anomaly detection engine and a real-time threat detection engine, wherein some of the first indicia of possible security breaches are detected by the real-time anomaly detection engine as security related anomalies and others of the first indicia are detected by the real-time threat detection engine as security related threats based on the detected anomalies; and
wherein the batch analysis engine includes a batch anomaly detection engine and a batch threat detection engine, wherein some of the second indicia of possible security breaches are detected by the batch anomaly detection engine as security related anomalies and others of the second indicia are detected by the batch threat detection engine as security related threats based on the anomalies detected by the batch anomaly detection engine.

9. The network security breach detection system as recited in claim 1 , wherein the second event data includes a larger amount of data than the first event data and has been generated over a longer time period than the first event data.

10. The network security breach detection system as recited in claim 1 , wherein:
the real-time analysis engine executes a first plurality of versions of a plurality of machine learning models to detect the first indicia of possible security breaches in the real-time mode; and
the batch analysis engine executes a second plurality of versions of said plurality of machine learning models to detect the second indicia of possible security breaches in the batch mode.

11. The network security breach detection system as recited in claim 1 , wherein:
the real-time analysis engine and the batch analysis engine collectively execute a plurality of machine learning models to detect the first indicia and second indicia of possible security breaches; and
the real-time analysis engine and the batch analysis engine share a model state of a particular machine learning model of the plurality of machine learning models.

12. The network security breach detection system as recited in claim 1 , wherein:
the real-time analysis engine and the batch analysis engine collectively execute a plurality of machine learning models to detect the first indicia and second indicia of possible security breaches; and
a result from the batch analysis engine is used to update a model state of a machine learning model used by the real-time analysis engine.

13. The network security breach detection system as recited in claim 1 , wherein:
the real-time analysis engine and the batch analysis engine collectively execute a plurality of machine learning models to detect the first indicia and second indicia of possible security breaches; and
a result from the real-time analysis engine is used to update a model state of a machine learning model used by the batch analysis engine.

14. The network security breach detection system as recited in claim 1 , wherein:
the real-time analysis engine and the batch analysis engine collectively execute a plurality of machine learning models to detect the first indicia and second indicia of possible security breaches;
a result from the batch analysis engine is used to update a model state of a machine learning model used by the real-time analysis engine; and
a result from the real-time analysis engine is used to update a model state of a machine learning model used by the batch analysis engine.

Assignees

• Splunk Inc

Inventors

• Muddu Sudhakar
• Tryfonas Christos
• Bulusu Ravi Prasad

Classifications

• G06N20/20 Primary

  Ensemble learning · CPC title

• H04L63/1416 Primary

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

• G06N7/01

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

• G06F40/134

  Hyperlinking · CPC title

• H04L41/22

  comprising specially adapted graphical user interfaces [GUI] · CPC title