IP Library › Granted Patent US 12,592,915
Granted Patent B2
US 12,592,915 · App. 18/526,253 · Granted Mar 31, 2026

Inference-based selective flow inspection

Inventors: Andrew E Ossipov (Lewisville, TX); James W. Kasper (Leander, TX)
Assignee: Cisco Technology, Inc.
H04L63/0428H04L63/1425
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Quick Facts
Patent No.
US 12,592,915
App. No.
18/526,253
Granted
Mar 31, 2026
Kind
B2
Abstract

Techniques for augmenting deep packet inspection capabilities of a network security device provisioned in a networked computing environment with inference-based flow selection to focus processing resources on network traffic that is likely to be malicious. The network device(s) may receive decryption policies comprising one or more decrypt and/or do not decrypt rules for applying the decryption policy to the network traffic. The network device may receive network traffic associated with a given connection flow through the network between a client device and a workload application, and the network device may determine whether to decrypt or refrain from decrypting the network traffic associated with the network flow based on a risk score that is generated by the network device using connection fingerprints associated with the client device and the workload application, respectively, based on behavioral characteristics of the client device and the workload, respectively.

Claims (78)

1 . A method comprising:

receiving, at a first network device associated with a network, a decryption policy comprising one or more traffic rules for decrypting network traffic associated with connection flows through the network;

receiving network traffic associated with a first connection flow between a first client device and a first application executing on a second network device of the network;

generating a first connection fingerprint associated with the first connection flow based at least in part on first behavioral characteristics of the first client device;

generating a second connection fingerprint associated with the first connection flow based at least in part second behavioral characteristics of the first application;

generating a first risk score associated with the first connection flow based at least in part on the first connection fingerprint and the second connection fingerprint; and

based at least in part on the first risk score and the decryption policy, one of:

decrypting, by the first network device, at least a portion of the network traffic associated with the first connection flow, wherein at least the portion of the network traffic is required to be decrypted to perform one or more security services with respect to the first connection flow; or

refraining from decrypting, by the first network device, the network traffic associated with the first connection flow.

2 . The method of claim 1 , wherein the first risk score indicates at least one of:

a first likelihood that the first connection flow is malicious;

a second likelihood that the first client device is malicious;

a third likelihood that the first application is malicious; and

a fourth likelihood that the second network device is malicious.

3 . The method of claim 1 , wherein the decryption policy includes at least a first traffic rule representing a first risk score threshold, and the method further comprising:

determining that the first risk score is less than the first risk score threshold; and

refraining from decrypting the network traffic associated with the first connection flow based at least in part on determining that the first risk score is less than the first risk score threshold.

4 . The method of claim 1 , wherein the decryption policy includes at least a first traffic rule representing a first risk score threshold, and the method further comprising:

determining that the first risk score is greater than the first risk score threshold; and

decrypting at least the portion of the network traffic associated with the first connection flow based at least in part on determining that the first risk score is greater than the first risk score threshold.

5 . The method of claim 1 , wherein the decryption policy includes at least a first traffic rule representing a first risk score threshold, and the method further comprising:

determining that the first application is a trusted application in the network;

determining that the first risk score is greater than the first risk score threshold; and

decrypting at least the portion of the network traffic associated with the first connection flow based at least in part on determining that the first risk score is greater than the first risk score threshold.

6 . The method of claim 1 , wherein the decryption policy includes at least a first traffic rule representing a first risk score threshold, and the method further comprising:

determining that the first risk score is less than the first risk score threshold;

determining that the first application is an untrusted application in the network; and

decrypting at least the portion of the network traffic associated with the first connection flow based at least in part on determining that the first application is an untrusted application in the network.

7 . The method of claim 1 , wherein the first behavioral characteristics comprise at least one of:

data fields of the first connection flow that are associated with the first client device;

historical data associated with the first client device, the historical data comprising previous connections flows associated with the first client device; or

an average inter-arrival time of the network traffic associated with the previous connection flows associated with the first client device.

8 . A system comprising:

one or more processors; and

one or more computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

receiving, at a first network device associated with a network, a decryption policy comprising one or more traffic rules for applying the decryption policy to network traffic associated with connection flows through the network;

receiving network traffic associated with a first connection flow between a first client device and a first application executing on a second network device of the network;

generating a connection pattern associated with the first connection flow based at least in part on first behavioral characteristics of the first client device and second behavioral characteristics of the first application;

generating a first risk score associated with the first connection flow based at least in part on the connection pattern associated with the first connection flow; and

decrypting, by the first network device, at least a portion of the network traffic based at least in part on the first risk score and the decryption policy, wherein at least the portion of the network traffic is required to be decrypted to perform one or more security services with respect to the first connection flow.

9 . The system of claim 8 , wherein the first risk score indicates a likelihood that at least one of the client device, the first application, or the second network device is malicious.

10 . The system of claim 8 , wherein the connection pattern is a first connection pattern, and the operations further comprising:

receiving network traffic associated with a second connection flow between the first client device and a second application executing on a third network device of the network;

generating a second connection pattern associated with the second connection flow based at least in part on the first behavioral characteristics of the first client device and third behavioral characteristics of the second application;

generating a second risk score associated with the second connection flow based at least in part on the second connection pattern associated with the second connection flow;

receiving the network traffic associated with the second connection flow; and

refraining from decrypting the network traffic associated with the second connection flow based at least in part on the second risk score and the decryption policy.

11 . The system of claim 8 , wherein the first behavioral characteristics comprise at least one of:

data fields of the first connection flow that are associated with the first client device;

historical data associated with the first client device, the historical data comprising previous connections flows associated with the first client device; or

an average inter-arrival time of the network traffic associated with the previous connection flows associated with the first client device.

12 . The system of claim 8 , wherein the first network device is a secure access gateway configured to apply security services to the network traffic, the security services comprising at least one of:

a firewall service; and

a deep packet inspection (DPI) service.

13 . The system of claim 8 , wherein the first network device is configured to route the network traffic between the first client device and the first application via the first connection flow.

14 . The system of claim 8 , wherein the decryption policy includes at least a first traffic rule representing a first risk score threshold, and the operations further comprising determining that the first risk score is less than the first risk score threshold, wherein refraining from decrypting the network traffic associated with the first connection flow is based at least in part on determining that the first risk score is less than the first risk score threshold.

15 . A method comprising: receiving, at a first network device associated with a network, a decryption policy comprising one or more traffic rules for applying the decryption policy to connection flows through the network;

receiving network traffic associated with a first connection flow between a first client device and a first application executing on a second network device of the network;

generating a connection pattern associated with the first connection flow based at least in part on first behavioral characteristics of first client device and second behavioral characteristics of the first application;

generating a first risk score associated with the first connection flow based at least in part on the connection pattern associated with the first connection flow;

and refraining from decrypting, by the first network device, at least a portion of the network traffic associated with the first connection flow based at least in part on the first risk score and the decryption policy, wherein at least the portion of the network traffic is decrypted to perform one or more security services with respect to the first connection flow.

16 . The method of claim 15 , wherein the connection pattern indicates a likelihood that at least one of the client device, the first application, or the second network device is malicious.

17 . The method of claim 15 , wherein the connection pattern is a first connection pattern, and the method further comprising:

receiving network traffic associated with a second connection flow between a second client device and the first application;

generating a second connection pattern associated with the second connection flow based at least in part on the second behavioral characteristics of the first application and third behavioral characteristics of the second client device;

generating a second risk score associated with the second connection flow based at least in part on the second connection pattern associated with the second connection flow; and

decrypting at least a portion of the network traffic associated with the second connection flow based at least in part on the second risk score and the decryption policy.

18 . The method of claim 15 , wherein the second behavioral characteristics comprise at least one of:

data fields of the connection flow that are associated with at least one of the first application or the second network device;

historical data associated with at least one of the first application or the second network device, the historical data comprising previous connections flows associated with at least one of the first application or the second network device; or

an average inter-arrival time of the network traffic associated with the previous connection flows associated with at least one of the first application or the second network device.

19 . The method of claim 15 , wherein the decryption policy includes at least a first traffic rule representing a first risk score threshold, and the method further comprising:

determining that the first risk score is greater than the first risk score threshold; and

decrypting at least a portion of the network traffic associated with the first connection flow based at least in part on determining that the first risk score is greater than the first risk score threshold.

20 . The method of claim 15 , wherein the decryption policy includes at least a first traffic rule representing a first risk score threshold, and the method further comprising:

determining that the first application is a trusted application in the network;

determining that the first risk score is greater than the first risk score threshold; and

decrypting at least a portion of the network traffic associated with the first connection flow based at least in part on determining that the first risk score is greater than the first risk score threshold.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 1, 2023
From: OSSIPOV, ANDREW E.; KASPER, JAMES W.
To: CISCO TECHNOLOGY, INC.
Reel/Frame 065732/0419 →
Continuity (2)
Provisional Application 63470534 · Jun 2, 2023
Related Publication 20240406147A1 · Dec 5, 2024
References Cited (21)
US 10580225B2 · Deshmukh · 2020 [cited by examiner]
US 10594721B1 · Scotney · 2020 [cited by examiner]
US 11196546B2 · Anderson · 2021 [cited by examiner]
US 20160088001A1 · Yeh et al. · 2016 [cited by applicant]
US 20170353433A1 · Antony · 2017 [cited by examiner]
US 20180139141A1 · Stepanek et al. · 2018 [cited by applicant]
US 20190297096A1 · Ahmed et al. · 2019 [cited by applicant]
US 20190312846A1 · Taylor et al. · 2019 [cited by applicant]
US 20200053103A1 · Rehak et al. · 2020 [cited by applicant]
US 20200067930A1 · Goeringer · 2020 [cited by examiner]
US 20200106740A1 · Bangalore Krishnamurthy · 2020 [cited by applicant]
US 20200302074A1 · Little · 2020 [cited by examiner]
US 20210194891A1 · Burke · 2021 [cited by examiner]
US 20210273948A1 · Miller · 2021 [cited by examiner]
US 20210273957A1 · Boyer et al. · 2021 [cited by applicant]
US 20220350913A1 · Shenefiel et al. · 2022 [cited by applicant]
US 20230156034A1 · Naidoo · 2023 [cited by examiner]
US 20240223434A1 · Cheng · 2024 [cited by examiner]
Muhammad et al., “Driving Toward Safety: A Personalized Approach to Traffic Risk Assessment Leveraging Driver Behaviour Data,” 2024 IEEE Smart World Congress (SWC) Year: 2024 | Conference Paper | Publisher: IEEE. [cited by examiner]
Trirat et al., “MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction,” IEEE Transactions on Intelligent Transportation Systems Year: 2023 | vol. 24, Issue: 4 | Journal Article | Publisher… [cited by examiner]
International Search Report and Written Opinion for International Application No. PCT/US2024/031307, mailed Oct. 1, 2024, 12 Pages. [cited by applicant]