Prioritizing bayes network alerts

• US 20030093514A1
• Filed: 09/13/2001
• Published: 05/15/2003
• Est. Priority Date: 09/13/2001
• Status: Active Grant

• Current Assignee: SRI International, Inc.
• Original Assignee: SRI International, Inc.
• Inventors: Valdes, Alfonso De Jesus, Fong, Martin Wayne, Porras, Phillip Andrew
• Granted Patent: US 7,379,993 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 63/1416 Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227

Abstract

This invention uses Bayesian techniques to prioritize alerts or alert groups generated by intrusion detection systems and other information security devices, such as network analyzers, network monitors, firewalls, antivirus software, authentication services, host and application security services, etc. In a preferred embodiment, alerts are examined for the presence of one or more relevant features, such as the type of an attack, the target of an attack, the outcome of an attack, etc. At least a subset of the features is then provided to a real-time Bayes network, which assigns relevance scores to the received alerts or alert groups. In another embodiment, a network manager (a person) can disagree with the relevance score assigned by the Bayes network, and give an alert or alert group a different relevance score. The Bayes network is then modified so that similar future alerts or alert groups will be assigned a relevance score that more closely matches the score given by the network manager.

Claims

1. In a computer network having an information security device that generates alerts when attacks or anomalous incidents are detected, a method for prioritizing alerts comprising the steps of:

receiving alerts from the information security device;

examining the received alerts for the presence of one or more relevant features;

providing a summary or list of the features from at least a subset of the received alerts to a Bayes network for analysis; and

assigning relevance scores to at least a subset of the received alerts, the relevance scores based at least in part on the analysis performed by the Bayes network.

2. The method of claim 1 wherein the features are selected from the following group:

attack or incident type;

attack or incident outcome;

attack or incident source;

the information security device'"'"'s confidence level in the attack or incident type and attack or incident outcome; and

network resources affected by the attack or incident.

3. The method of claim 1 wherein the Bayes network uses conditional probability tables (CPTs) to model potential influence of the alert features on the relevance scores.

4. The method of claim 3 wherein the CPTs include network information selected from the following group:

the network'"'"'s operating system vulnerabilities;

the network'"'"'s hardware vulnerabilities;

the network'"'"'s application vulnerabilities; and

the relative importance of network resources and services to effective operation of the network.

5. The method of claim 4 further comprising the steps of:

comparing the relevance score with a second relevance score provided by a network operator; and

adding a new row, weighted towards the second relevance score, to one or more of the CPTs.

6. In a computer network that has a plurality of information security devices, each of which generates alerts when attacks or anomalous incidents are detected, a method for prioritizing groups of related alerts comprising the steps of:

receiving the groups of related alerts;

examining the received groups for the presence of one or more relevant features;

providing a summary or list of the features from at least a subset of the received groups to a Bayes network for analysis; and

assigning relevance scores to at least a subset of the received groups, the relevance scores based at least in part on the analysis performed by the Bayes network.

7. In a computer network having an information security device that generates alerts when attacks or anomalous incidents are detected, a method for assigning a relevance score to alerts comprising the steps of:

receiving a first alert;

examining the first alert for the presence of one or more relevant features;

providing a summary or list of the features from the first alert to a Bayes network for analysis;

assigning a relevance score to the first alert, the relevance score based at least in part on the analysis performed by the Bayes network;

receiving a second relevance score from a network operator; and

modifying the Bayes network such that when a subsequent alert similar to the first alert is analyzed by the Bayes network, the subsequent alert is assigned a relevance score that more closely matches the second relevance score.