IP Library Granted Patent US 9,032,521
Granted Patent B2
US 9,032,521 · App. 12/903,525 · Granted May 12, 2015

Adaptive cyber-security analytics

Inventors: Lisa Amini (Yorktown Heights, NY); Mihai Christodorescu (Briarcliff Manor, NY); Mitchell A. Cohen (Yorktown Heights, NY); Srinivasan Parthasarathy (Yonkers, NY); Josyula Rao (Briarcliff Manor, NY); Reiner Sailer (Scarsdale, NY); Douglas L. Schales (Armonk, NY); Wietse Z. Venema (Amawalk, NY); Oliver Verscheure (Dunboyne, IE)
Assignee: International Business Machines Corporation
H04L63/1433
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 9,032,521
App. No.
12/903,525
Granted
May 12, 2015
Kind
B2
Abstract

Performing adaptive cyber-security analytics including a computer implemented method that includes receiving a report on a network activity. A score responsive to the network activity and to a scoring model is computed at a computer. The score indicates a likelihood of a security violation. The score is validated and the scoring model is automatically updated responsive to results of the validating. The network activity is reported as suspicious in response to the score being within a threshold of a security violation value.

Claims (43)

1. A computer implemented method for performing security analytics, the method comprising:

receiving a report on a network activity in a network;

calculating, at a computer, a score responsive to the network activity and to a scoring model, the score indicating a likelihood of a security violation;

validating the score, wherein validating includes probing of one or more hosts in the network and determining if the security violation is a true alert or a false positive;

automatically updating the scoring model responsive to determining that the security violation is a true alert; and

reporting the network activity as suspicious in response to the score being within a threshold of a security violation value.

2. The method of claim 1 , wherein the calculating is further responsive to a targeted detector and to a behavioral anomaly detector.

3. The method of claim 1 , wherein the calculating is further responsive to at least one pattern of a known bad action.

4. The method of claim 1 , wherein the calculating is further responsive to a behavioral anomaly detector.

5. The method of claim 1 , wherein the automatically updating the scoring model comprises utilizing feature extraction algorithms for constructing anomaly detectors.

6. The method of claim 1 , wherein the automatically updating the scoring model comprises utilizing discriminative pattern extraction for constructing a targeted detector.

7. The method of claim 1 , wherein the automatically updating the scoring model comprises utilizing concept drift detection algorithms for monitoring the scoring model.

8. The method of claim 1 , wherein the automatically updating the scoring model comprises utilizing a learning algorithm.

9. The method of claim 1 , wherein the validating comprises utilizing a time delayed alert.

10. A computer program product for performing security analytics, the computer program product comprising:

a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising:

receiving a report on a network activity in a network,

calculating a score responsive to the network activity and to a scoring model, the score indicating a likelihood of a security violation;

validating the score, wherein validating includes probing of one or more hosts in the network and determining if the security violation is a true alert or a false positive;

automatically updating the scoring model responsive to determining that the security violation is a true alert; and

reporting the network activity as suspicious in response to the score being within a threshold of a security violation value.

11. The computer program product of claim 10 , wherein the calculating is further responsive to a targeted detector and to a behavioral anomaly detector.

12. The computer program product of claim 10 , wherein the calculating is further responsive to at least one pattern of a known bad action.

13. The computer program product of claim 10 , wherein the calculating is further responsive to a behavioral anomaly detector.

14. The computer program product of claim 10 , wherein the automatically updating the scoring model comprises utilizing feature extraction algorithms for constructing anomaly detectors.

15. The computer program product of claim 10 , wherein the automatically updating the scoring model comprises utilizing discriminative pattern extraction for constructing a targeted detector.

16. The computer program product of claim 10 , wherein the automatically updating the scoring model comprises utilizing concept drift detection algorithms for monitoring the scoring model.

17. The computer program product of claim 10 , wherein the automatically updating the scoring model comprises utilizing a learning algorithm.

18. The computer program product of claim 10 , wherein the validating comprises utilizing a time delayed alert.

19. A system for performing security analytics, the system comprising:

a computer; and

security analytics application executable by the computer, the security analytics application performing a method comprising:

receiving a report on a network activity in a network,

calculating, at a computer, a score responsive to the network activity and to a scoring model, the score indicating a likelihood of a security violation;

validating the score, wherein validating includes probing of one or more hosts in the network and determining if the security violation is a true alert or a false positive;

automatically updating the scoring model responsive to determining that the security violation is a true alert; and

reporting the network activity as suspicious in response to the score being within a threshold of a security violation value.

20. The system of claim 19 , wherein the calculating is further responsive to a targeted detector and to a behavioral anomaly detector.

21. The system of claim 19 , wherein the automatically updating the scoring model comprises utilizing feature extraction algorithms for constructing anomaly detectors.

22. The system of claim 19 , wherein the automatically updating the scoring model comprises utilizing discriminative pattern extraction for constructing a targeted detector.

23. The system of claim 19 , wherein the automatically updating the scoring model comprises utilizing concept drift detection algorithms for monitoring the scoring model.

24. The system of claim 19 , wherein the automatically updating the scoring model comprises utilizing a learning algorithm.

25. The system of claim 19 , wherein the validating comprises utilizing a time delayed alert.

Assignments (2)
CORRECTIVE ASSIGNMENT TO CORRECT THE FIRST NAME ON RECORDATION SHEET FOR INVENTOR VERSCHEURE OLIVER "SHOULD READ" OLIVIER PREVIOUSLY RECORDED ON REEL 025132 FRAME 0443. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Nov 3, 2010
From: AMINI, LISA; CHRISTODORESCU, MIHAI; COHEN, MITCHELL A.; PARTHASARATHY, SRINIVASAN; RAO, JOSYULA; SAILER, REINER; SCHALES, DOUGLAS L.; VENEMA, WIETSE Z.; VERSCHEURE, OLIVIER
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 025239/0022 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 13, 2010
From: AMINI, LISA; CHRISTODORESCU, MIHAI; COHEN, MITCHELL A.; PARTHASARATHY, SRINIVASAN; RAO, JOSYULA; SAILER, REINER; SCHALES, DOUGLAS L.; VENEMA, WIETSE Z.; VERSCHEURE, OLIVER
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 025132/0443 →
Continuity (1)
Related Publication 20120096549A1 · Apr 19, 2012