Interface providing an interactive trendline for a detected threat to facilitate evaluation for false positives

• US 10,666,668 B2
• Filed: 01/28/2019
• Issued: 05/26/2020
• Est. Priority Date: 08/31/2015
• Status: Active Grant

• Current Assignee: Splunk Inc. (Cisco Systems, Inc.)
• Original Assignee: Splunk Inc. (Cisco Systems, Inc.)
• Inventors: Muddu, Sudhakar, Tryfonas, Christos
• Primary Examiner(s): Naghdali, Khalil
• Application Number:
  US16/259,999
• Publication Number:
  US 20190158517A1
• Time in Patent Office: 484 Days
• Field of Search: 726 23
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/444 Spatial browsing, e.g. 2D maps, 3D or virtual spaces
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/0484 for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04847 Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G06F 40/134 Hyperlinking
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06V 10/225 based on a marking or identifier characterising the area
• CPC Class Codes: H04L 2463/121 Timestamp
• CPC Class Codes: H04L 41/0893 Assignment of logical groups to network elements
• CPC Class Codes: H04L 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 43/00 : Arrangements for monitoring or testing data switching networks
• CPC Class Codes: H04L 43/045 : for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/062 : related to network traffic
• CPC Class Codes: H04L 43/08 : Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 63/06 : for supporting key management in a packet data network cryptographic mechanisms or cryptographic arrangements for key management H04L9/08
• CPC Class Codes: H04L 63/1408 : by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1416 : Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/1425 : Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 63/1433 : Vulnerability analysis
• CPC Class Codes: H04L 63/1441 : Countermeasures against malicious traffic countermeasures against attacks on cryptographic mechanisms H04L9/002
• CPC Class Codes: H04L 63/20 : for managing network security; network security policies in general filtering policies H04L63/0227

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.

Claims

1. A method comprising:

receiving event data generated by network activities of entities that interact with a computer network, wherein the event data comprises machine data, and the entities include computer users and/or devices in communication with the network;

based upon the received event data as it is received,

(i) automatically detecting anomalies indicating deviations from expected or permitted network activities, wherein each anomaly is classified by type and is associated with an entity or entities that participated in network activities and a date at which the detected anomaly occurred, and

(ii) automatically detecting threats based upon at least one of a number, type, or timing of detected anomalies, and generating a listing of detected threats as pending threats against the computer network;

upon receiving a user-selection of a detected threat, causing display, in a graphical user interface, of an interactive trendline, which indicates changes to the number of the occurrences of anomalies as a function of dates along the trendline to enable a user to visually depict a trend of the occurrences of the anomalies associated with the threat; and

upon receiving a user-selection, via the graphical user interface, to resolve the detected threat as a false positive, deleting the threat from the listing of pending threats.

2. The method of claim 1, wherein the automated determinations result from execution of machine learning logic.

3. The method of claim 1, wherein the trendline is a line connecting a set of points, wherein each point indicates a number of each anomalies occurring on a corresponding date.

4. The method of claim 1, further comprising:

upon receiving a selection of a point on the trend line, identifying each anomaly occurring on the respective date; and

upon selection of an identified anomaly, causing display of the event data that triggered detection of the anomaly.

5. The method of claim 1, further comprising:

upon receiving a selection of a point on the trend line, identifying each anomaly occurring on the respective date; and

upon receiving a selection of an anomaly from the listing of anomalies, causing display of a graphical representation of a relationship between the entities whose network activity triggered detection of the anomaly.

6. The method of claim 1, wherein each anomaly is classified as a type from a set of anomaly types, the set of types including at least one type pertaining to an alarm, an excessive data transfer, or an unusual login time.

7. The method of claim 1, further comprising:

upon receiving the selection of a threat, causing additional display of a graphical representation of a relationship between the entities participating in the network activities that triggered the threat, wherein the display includes one or more lines that connect the entities whose participation together in a network activity triggered an anomaly.

8. The method of claim 1, wherein the anomalies are detected in real-time.

9. The method of claim 1, wherein the anomalies are detected based on both real-time detection and batch detection.

10. A non-transitory, computer-readable storage medium storing instructions, an execution of which in a computer system causes the computer system to perform operations comprising:

receiving event data generated by network activities of entities that interact with a computer network, wherein the event data comprises machine data, and the entities include computer users and/or devices in communication with the network;

based upon the received event data as it is received,

(i) automatically detecting anomalies indicating deviations from expected or permitted network activities, wherein each anomaly is classified by type and is associated with an entity or entities that participated in network activities and a date at which the detected anomaly occurred, and

(ii) automatically detecting threats based upon at least one of a number, type, or timing of detected anomalies, and generating a listing of detected threats as pending threats against the computer network;

upon receiving a user-selection of a detected threat, causing display, in a graphical user interface, of an interactive trendline, which indicates changes to the number of the occurrences of anomalies as a function of dates along the trendline to enable a user to visually depict a trend of the occurrences of the anomalies associated with the threat; and

upon receiving a user-selection, via the graphical user interface, to resolve the detected threat as a false positive, deleting the threat from the listing of pending threats.

11. The computer-readable storage medium of claim 10, wherein the automated determinations result from execution of machine learning logic.

12. The computer-readable storage medium of claim 10, wherein the trendline is a line connecting a set of points, wherein each point indicates a number of each anomalies occurring on a corresponding date.

13. The computer-readable storage medium of claim 10, performing operations further comprising:

upon receiving a selection of a point on the trend line, identifying each anomaly occurring on the respective date; and

upon selection of an identified anomaly, causing display of the event data that triggered detection of the anomaly.

14. The computer-readable storage medium of claim 10, performing operations further comprising:

upon receiving a selection of a point on the trend line, identifying each anomaly occurring on the respective date; and

upon receiving a selection of an anomaly from the listing of anomalies, causing display of a graphical representation of a relationship between the entities whose network activity triggered detection of the anomaly.

15. The computer-readable storage medium of claim 10, wherein each anomaly is classified as a type from a set of anomaly types, the set of types including at least one type pertaining to an alarm, an excessive data transfer, or an unusual login time.

16. The computer-readable storage medium of claim 10, performing operations further comprising:

upon receiving the selection of a threat, causing additional display of a graphical representation of a relationship between the entities participating in the network activities that triggered the threat, wherein the display includes one or more lines that connect the entities whose participation together in a network activity triggered an anomaly.

17. The computer-readable storage medium of claim 10, wherein the anomalies are detected in real-time.

18. The computer-readable storage medium of claim 10, wherein the anomalies are detected based on both real-time detection and batch detection.

19. A computer system comprising:

computer memory for storing machine data; and

a processor for;

receiving event data generated by network activities of entities that interact with a computer network, wherein the event data comprises machine data, and the entities include computer users and/or devices in communication with the network;

based upon the received event data as it is received,

(i) automatically detecting anomalies indicating deviations from expected or permitted network activities, wherein each anomaly is classified by type and is associated with an entity or entities that participated in network activities and a date at which the detected anomaly occurred, and

(ii) automatically detecting threats based upon at least one of a number, type, or timing of detected anomalies, and generating a listing of detected threats as pending threats against the computer network;

upon receiving a user-selection of a detected threat, causing display, in a graphical user interface, of an interactive trendline, which indicates changes to the number of the occurrences of anomalies as a function of dates along the trendline to enable a user to visually depict a trend of the occurrences of the anomalies associated with the threat; and

upon receiving a user-selection, via the graphical user interface, to resolve the detected threat as a false positive, deleting the threat from the listing of pending threats.

20. The computer system of claim 19, wherein the automated determinations result from execution of machine learning logic.