AUTOMATICALLY GENERATING NETWORK RESOURCE GROUPS AND ASSIGNING CUSTOMIZED DECOY POLICIES THERETO

• US 20170310689A1
• Filed: 07/05/2017
• Published: 10/26/2017
• Est. Priority Date: 06/08/2015
• Status: Active Application

• Current Assignee: illusive networks LTD.
• Original Assignee: illusive networks LTD.
• Inventors: Touboul, Shlomo, Levin, Hanan, Roubach, Stephane, Mischari, Assaf, Ben David, Itai, Avraham, Itay, Ozer, Adi, Kazaz, Chen, Israeli, Ofer, Vingurt, Olga, Gareh, Liad, Grimberg, Israel, Cohen, Cobby, Sultan, Sharon, Kubovsky, Matan
• Granted Patent: US 10,291,650 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 21/55 Detecting local intrusion or implementing counter-measures
• CPC Class Codes: G06F 21/554 involving event detection and direct action
• CPC Class Codes: G06F 21/56 Computer malware detection or handling, e.g. anti-virus arrangements
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 2463/146 Tracing the source of attacks
• CPC Class Codes: H04L 63/10 for controlling access to devices or network resources
• CPC Class Codes: H04L 63/102 Entity profiles
• CPC Class Codes: H04L 63/1416 Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 63/1433 Vulnerability analysis
• CPC Class Codes: H04L 63/1441 Countermeasures against malicious traffic countermeasures against attacks on cryptographic mechanisms H04L9/002
• CPC Class Codes: H04L 63/1491 using deception as countermeasure, e.g. honeypots, honeynets, decoys or entrapment
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227

Abstract

A cyber security system comprising circuitry of a decoy deployer planting one or more decoy lateral attack vectors in each of a first and a second group of resources within a common enterprise network of resources, the first and second groups of resources having different characteristics in terms of subnets, naming conventions, DNS aliases, listening ports, users and their privileges, and installed applications, wherein a lateral attack vector is an object of a first resource within the network that has a potential to be used by an attacker who discovered the first resource to further discover information regarding a second resource within the network, the second resource being previously undiscovered by the attacker, and wherein the decoy lateral attack vectors in the first group conform to the characteristics of the first group, and the decoy lateral attack vectors in the second group conform to the characteristics of the second group.

Claims

1-8. -8. (canceled)

9. A cyber security system to detect attackers, comprising circuitry of a decoy deployer planting one or more decoy lateral attack vectors in each of a first and a second group of resources within a common enterprise network of resources, the first and second groups of resources having different characteristics in terms of subnets, naming conventions, DNS aliases, listening ports, users and their privileges, and installed applications, wherein a lateral attack vector is an object of a first resource within the network that has a potential to be used by an attacker who discovered the first resource to further discover information regarding a second resource within the network, the second resource being previously undiscovered by the attacker, and wherein the decoy lateral attack vectors in the first group conform to the characteristics of the first group, and the decoy lateral attack vectors in the second group conform to the characteristics of the second group.

10. The system of claim 9 further comprising circuitry of a learning module analyzing characteristics of the common enterprise network of resources, and deriving therefrom the grouping of the resources into the first and second groups.

11. The system of claim 9 further comprising circuitry of a policy manager assigning a customized decoy policy to each group of resources, wherein a decoy policy for a group of resources comprises one or more decoy lateral attack vectors, and one or more resources in the group in which the one or more decoy lateral attack vectors are to be planted.

13. The method of claim 11 further comprising:

analyzing characteristics of the common enterprise network of resources; and

deriving therefrom the grouping of the resources into the first and second groups.

14. The method of claim 11 further comprising assigning a customized decoy policy to each group of resources, wherein a decoy policy for a group of resources comprises one or more decoy lateral attack vectors, and one or more resources in the group in which the one or more decoy lateral attack vectors are to be planted.

12. A cyber security method for detecting attackers, comprising planting one or more decoy lateral attack vectors in each of a first and a second group of resources within a common enterprise network of resources, the first and second groups of resources having different characteristics in terms of subnets, naming conventions, DNS aliases, listening ports, users and their privileges, and installed applications, wherein a lateral attack vector is an object of a first resource within the network that has a potential to be used by an attacker who discovered the first resource to further discover information regarding a second resource within the network, the second resource being previously undiscovered by the attacker, and wherein the decoy lateral attack vectors in the first group conform to the characteristics of the first group, and the decoy lateral attack vectors in the second group conform to the characteristics of the second group.

15. A network analysis method, comprising:

analyzing characteristics of an enterprise network of resources, the characteristics comprising subnets, naming conventions, DNS aliases, listening ports, users and their privileges, and installed applications; and

deriving from said analyzing a grouping of the enterprise network into first and second groups of resources, wherein the first and second groups have different characteristics.