Ml based domain risk scoring and its applications to advanced url filtering

Patent metadata

Field	Value
Publication number	US-2025358300-A1
Application number	US-202418667982-A
Country	US
Kind code	A1
Filing date	May 17, 2024
Priority date	May 17, 2024
Publication date	Nov 20, 2025
Grant date	—

Abstract

The present application discloses a method, system, and computer system for providing real-time detection of malicious URLs based on a machine-learning powered domain risk scoring. The method includes (i) identifying a subset of higher risk websites, wherein the higher risk websites are at risk for potential malware injection or modification, and (ii) in response to identifying the subset of higher risk websites, performing an active measure based at least in part on the identified subset of higher risk websites.

First claim

What is claimed is:

1 . A system, comprising:
one or more processors configured to:
identify a subset of higher risk websites based on using a classifier, wherein the higher risk websites are at risk for potential malware injection or modification; and
in response to identifying the subset of higher risk websites, perform an active measure based at least in part on the identified subset of higher risk websites; and
a memory coupled to the one or more processors and configured to provide the one or more processors with instructions.

2 . The system of claim 1 , wherein the classifier is a machine learning model.

3 . The system of claim 2 , wherein the machine learning model comprises a random forest machine learning model.

4 . The system of claim 1 , wherein performing the active measure in response to determining that a candidate domain is comprised in the subset of higher risk websites comprises:
applying a security policy based on a classification of the candidate domain as being a higher risk website.

5 . The system of claim 4 , wherein applying the security policy comprises:
handling network traffic to/from the candidate domain based at least in part on (i) a classification that the candidate domain is a higher risk website, and (ii) the security policy.

6 . The system of claim 1 , wherein the active measure comprises storing a set of classifications for the subset of higher risk website in a domain classification database.

7 . The system of claim 6 , wherein the domain classification database is used to detect higher risk website and in response to detection of the higher risk website, enforcing a security policy for handling traffic to or from the higher risk website.

8 . The system of claim 6 , wherein the one or more processors are further configured to:
obtain a candidate sample to be classified; infer a classification for the candidate sample based at least in part on querying the domain classification database; and perform an action based at least in part on the classification for the candidate sample.

9 . The system of claim 1 , wherein the subset of higher risk websites comprises one or more subdomains and one or more registered domains.

10 . The system of claim 1 , wherein the classifier is used to provide real-time analysis of a risk level for a candidate domain associated with a URL.

11 . The system of claim 10 , wherein the classifier used to provide real-time analysis is a lightweight inline machine learning model.

12 . The system of claim 11 , wherein the lightweight inline machine learning model is trained using a fewer number of features than an offline machine learning model that provides offline detection or classification of websites.

13 . The system of claim 1 , wherein the subset of higher risk websites are periodically crawled at a more frequent rate than websites classified as benign or low or medium risk.

14 . The system of claim 1 , wherein the one or more processors are further configured to:
in response to classifying a candidate website as a higher risk website,
causing the candidate website to be crawled; and
causing the candidate website to be analyzed for malware based at least in part on results of crawling the candidate website.

15 . The system of claim 1 , wherein the classifier comprises a rentable domain classifier and a non-rentable domain classifier.

16 . The system of claim 15 , wherein the rentable domain classifier is used to classify a candidate website in response to determining that a corresponding domain is a rentable domain.

17 . The system of claim 15 , wherein the non-rentable domain classifier is used to classify a candidate website in response to determining that a corresponding domain is a non-rentable domain.

18 . The system of claim 15 , wherein the rentable domain classifier and the non-rentable domain classifiers comprise machine learning models that are trained using different sets of features.

19 . The system of claim 1 , wherein the classifier is configured to predict whether a candidate domain is likely to become malicious within a predetermined period of time.

20 . The system of claim 19 , wherein the classifier assigns a risk score based on a likelihood that the candidate domain will become malicious within the predetermined period of time.

21 . The system of claim 20 , wherein the risk score is based at least in part on a machine learning-based computation that incorporates information from multiple data sources.

22 . The system of claim 1 , wherein the classifier comprises one or more of (i) an inline rentable domain classifier, (ii) an offline rentable domain classifier, (iii) an inline non-rentable domain classifier, and (iv) an offline non-rentable domain classifier.

23 . The system of claim 1 , wherein the classifier is an offline classifier that performs classifications offline that is asynchronous to an interception of network traffic.

24 . The system of claim 1 , wherein the classifier is an inline classifier that generates classifications contemporaneous with an interception and handling of network traffic.

25 . The system of claim 24 , wherein the inline classifier generates the classifications in less than 100 ms.

26 . The system of claim 1 , wherein the one or more processors are further configured to:
obtain a predicted classification for a candidate website from the classifier; perform a post-filtering to be performed with respect to the predicted classification to determine whether the predicted classification is a false positive classification; and determining a classification for the candidate website based at least in part on a result of post-filtering the predicted classification.

27 . A method, comprising:
identifying a subset of higher risk websites, wherein the higher risk websites are at risk for potential malware injection or modification; and in response to identifying the subset of higher risk websites, performing an active measure based at least in part on the identified subset of higher risk websites.

28 . A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for:
identifying a subset of higher risk websites, wherein the higher risk websites are at risk for potential malware injection or modification; and in response to identifying the subset of higher risk websites, performing an active measure based at least in part on the identified subset of higher risk websites.

29 . A system, comprising:
one or more processors configured to:
collect a set of features for a set of training sample websites, the set of training sample websites comprising a subset of benign or low risk domains, and a subset of high risk domains; and
perform a machine learning process to generate a domain classifier based at least in part on the set of features for the set of training sample websites;
deploy the domain classifier in a system to perform detection of malicious domains; and
a memory coupled to the one or more processors and configured to provide the one or more processors with instructions.

30 . The system of claim 29 , wherein the set of features are generated based at least in part one or more of crawled website content, lexical data, registration historical risk scores, pDNS data, an

Assignees

• Palo Alto Networks Inc

Inventors

• Mohamed Nabeel Mohamed Yoosuf
• Melicher William Russell
• Starov Oleksii
• Chen Zhenhua

Classifications

• H04L63/1425 Primary

  Traffic logging, e.g. anomaly detection · CPC title

• H04L63/1416 Primary

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