IP Library Granted Patent US 11,811,785
Granted Patent B2
US 11,811,785 · App. 17/496,061 · Granted Nov 7, 2023

Real-time scanning of IP addresses

Inventors: Huy Dang Ho (San Diego, CA); Hal Lonas (San Diego, CA); Trung Tran (San Diego, CA)
Assignee: OPEN TEXT INC
H04L63/14G06F21/577H04L43/18H04L43/50H04L61/2503H04L63/1433H04L2101/668
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Quick Facts
Patent No.
US 11,811,785
App. No.
17/496,061
Granted
Nov 7, 2023
Kind
B2
Abstract

Examples of the present disclosure describe systems and methods of providing real-time scanning of IP addresses. In aspects, input may be received by a real-time IP scanning system. The system may generate one or more work orders based on the input. A scanner associated with the system may access a work order and attempt to communicate with one or more devices identified by the work order. If the attempted communication with a device is successful, a protocol analyzer may be used to provide a predefined payload to the device. If the response from the device matches an expected string, the device may be determined to be a safe and/or legitimate device. If the response from the device does not match an expected string, the device may be determined to be a malicious device.

Claims (40)

1. A method for providing real-time scanning of IP addresses, the method comprising:

receiving target internet protocol (IP) information from a client device and identifying a computing device based on the target IP information;

communicating with the computing device to obtain device indicator information from the computing device;

providing the device indicator information to a first machine learned model to obtain a selection of a payload;

providing the selected payload to the computing device to solicit a response from the computing device;

receiving a payload response from the computing device; and

determining, based on the payload response, that the computing device is a malicious device.

2. The method of claim 1 , wherein determining that the computing device is the malicious device comprises providing the payload response to a second machine learned model and receiving from the second machine learned model an indication based on known data that the computing device is the malicious device.

3. The method of claim 1 , wherein receiving the target IP information from the client device comprises receiving a request from the client device via a user interface.

4. The method of claim 3 , wherein identifying the computing device in the target IP information comprises parsing the request received from the client device to identify the target IP information in the request.

5. The method of claim 4 , wherein identifying the computing device based on the target IP information further comprises generating one or more work orders and storing the one or more work orders in a work order queue.

6. The method of claim 5 , wherein identifying the computing device based on the target IP information comprises selecting a first one of the one or more work orders in the work order queue and processing the first one of the one or more work orders to identify the computing device.

7. The method of claim 1 , wherein the target IP information comprises at least one of an IP address, a port, a service and a protocol.

8. A system comprising:

a processor; and

memory coupled to the processor, the memory comprising computer executable instructions that, when executed by the processor, performs a method for providing real-time scanning of client devices, the method comprising:

receiving target internet protocol (IP) information from a client device and identifying a computing device based on the target IP information;

communicating with the computing device to obtain device indicator information from the computing device;

providing the device indicator information to a first machine learned model to obtain a selection of a payload;

providing the selected payload to the computing device to solicit a response from the computing device;

receiving a payload response from the computing device; and

determining, based on the payload response, that the computing device is a malicious device.

9. The system of claim 8 , wherein determining that the computing device is the malicious device comprises providing the payload response to a second machine learned model and receiving from the second machine learned model an indication based on known data that the computing device is the malicious device.

10. The system of claim 8 , wherein receiving the target IP information from the client device comprises receiving a request from the client device via a user interface.

11. The system of claim 10 , wherein identifying the computing device in the target IP information comprises parsing the request received from the client device to identify the target IP information in the request.

12. The system of claim 11 , wherein identifying the computing device based on the target IP information further comprises generating one or more work orders and storing the one or more work orders in a work order queue.

13. The system of claim 12 , wherein identifying the computing device based on the target IP information comprises selecting a first one of the one or more work orders in the work order queue and processing the first one of the one or more work orders to identify the computing device.

14. The system of claim 8 , wherein the target IP information comprises at least one of an IP address, a port, a service and a protocol.

15. A computer program product comprising a non-transitory computer-readable medium storing computer executable instructions that when executed cause a computing system to perform a method for providing real-time scanning of IP addresses, the method comprising:

receiving target internet protocol (IP) information from a client device and identifying a computing device based on the target IP information;

communicating with the computing device to obtain device indicator information from the computing device;

providing the device indicator information to a first machine learned model to obtain a selection of a payload;

providing the selected payload to the computing device to solicit a response from the computing device;

receiving a payload response from the computing device; and

determining, based on the payload response, that the computing device is a malicious device.

16. The computer program product of claim 15 , wherein determining that the computing device is the malicious device comprises providing the payload response to a second machine learned model and receiving from the second machine learned model an indication based on known data that the computing device is the malicious device.

17. The computer program product of claim 15 , wherein receiving the target IP information from the client device comprises receiving a request from the client device via a user interface.

18. The computer program product of claim 17 , wherein identifying the computing device in the target IP information comprises parsing the request received from the client device to identify the target IP information in the request.

19. The computer program product of claim 18 , wherein identifying the computing device based on the target IP information further comprises generating one or more work orders and storing the one or more work orders in a work order queue.

20. The computer program product of claim 19 , wherein identifying the computing device based on the target IP information comprises selecting a first one of the one or more work orders in the work order queue and processing the first one of the one or more work orders to identify the computing device.

Assignments (4)
ASSIGNMENT AND ASSUMPTION AGREEMENT Recorded Jul 6, 2023
From: CARBONITE, LLC
To: OPEN TEXT INC.
Reel/Frame 064351/0178 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 29, 2023
From: WEBROOT LLC
To: CARBONITE, LLC
Reel/Frame 064167/0129 →
CERTIFICATE OF CONVERSION Recorded Jun 29, 2023
From: WEBROOT INC.
To: WEBROOT LLC
Reel/Frame 064176/0622 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 25, 2021
From: HO, HUY DANG; LONAS, HAL; TRAN, TRUNG
To: WEBROOT INC.
Reel/Frame 057903/0152 →
Continuity (4)
Continuation 16791635 · Feb 14, 2020
Continuation 15063590 · Mar 8, 2016
Provisional Application 62267431 · Dec 15, 2015
Related Publication 20220030007A1 · Jan 27, 2022