DETERMINING A REPUTATION OF A NETWORK ENTITY

• US 20190260653A1
• Filed: 05/06/2019
• Published: 08/22/2019
• Est. Priority Date: 06/05/2015
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Yadav, Navindra, Rao, Supreeth Hosur Nagesh, Kulshreshtha, Ashutosh, Madani, Omid, Pang, Jackson Ngoc Ki, Deen, Khawar, Scheib, Ellen Ellen
• Granted Patent: US 10,623,284 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/122 using management policies backup systems G06F11/1446; file migration policies for HSM systems G06F16/185
• CPC Class Codes: G06F 16/137 Hash-based content-based indexing of textual data G06F16/31
• CPC Class Codes: G06F 16/162 Delete operations erasing in storage systems G06F3/0652
• CPC Class Codes: G06F 16/17 Details of further file system functions
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• CPC Class Codes: G06F 16/174 Redundancy elimination performed by the file system management of the data involved in backup or backup restore using de-duplication of the data G06F11/14
• CPC Class Codes: G06F 16/1744 using compression, e.g. sparse files
• CPC Class Codes: G06F 16/1748 De-duplication implemented within the file system, e.g. based on file segments de-duplication techniques in storage systems for the management of data blocks G06F3/0641
• CPC Class Codes: G06F 16/2322 using timestamps
• CPC Class Codes: G06F 16/235 Update request formulation
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/288 Entity relationship models
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 2009/4557 Distribution of virtual machine instances; Migration and load balancing
• CPC Class Codes: G06F 2009/45587 Isolation or security of virtual machine instances
• CPC Class Codes: G06F 2009/45591 Monitoring or debugging support
• CPC Class Codes: G06F 2009/45595 : Network integration; Enabling network access in virtual machine instances
• CPC Class Codes: G06F 21/53 : by executing in a restricted environment, e.g. sandbox or secure virtual machine
• CPC Class Codes: G06F 21/552 : involving long-term monitoring or reporting
• CPC Class Codes: G06F 21/556 : involving covert channels, i.e. data leakage between processes inhibiting the analysis of circuitry or operation with measures against power attack G06F21/755
• CPC Class Codes: G06F 21/566 : Dynamic detection, i.e. detection performed at run-time, e.g. emulation, suspicious activities
• CPC Class Codes: G06F 2221/033 : Test or assess software
• CPC Class Codes: G06F 2221/2101 : Auditing as a secondary aspect
• CPC Class Codes: G06F 2221/2105 : Dual mode as a secondary aspect
• CPC Class Codes: G06F 2221/2111 : Location-sensitive, e.g. geographical location, GPS
• CPC Class Codes: G06F 2221/2115 : Third party
• CPC Class Codes: G06F 2221/2145 : Inheriting rights or properties, e.g., propagation of permissions or restrictions within a hierarchy
• CPC Class Codes: G06F 3/0482 : Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/04842 : Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04847 : Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G06F 9/45558 : Hypervisor-specific management and integration aspects
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 99/00 : Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G06T 11/206 : Drawing of charts or graphs
• CPC Class Codes: H04J 3/0661 : using timestamps
• CPC Class Codes: H04J 3/14 : Monitoring arrangements for SDH/SONET rings H04J3/085
• CPC Class Codes: H04L 1/242 : by comparing a transmitted test signal with a locally generated replica
• CPC Class Codes: H04L 41/046 : comprising network management agents or mobile agents therefor
• CPC Class Codes: H04L 41/0668 : by dynamic selection of recovery network elements, e.g. replacement by the most appropriate element after failure
• CPC Class Codes: H04L 41/0803 : Configuration setting
• CPC Class Codes: H04L 41/0806 : for initial configuration or provisioning, e.g. plug-and-play
• CPC Class Codes: H04L 41/0816 : the condition being an adaptation, e.g. in response to network events
• CPC Class Codes: H04L 41/0893 : Assignment of logical groups to network elements
• CPC Class Codes: H04L 41/0894 : Policy-based network configuration management
• CPC Class Codes: H04L 41/12 : Discovery or management of network topologies
• CPC Class Codes: H04L 41/16 : using machine learning or artificial intelligence
• CPC Class Codes: H04L 41/22 : comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 41/40 : using virtualisation of network functions or resources, e.g. SDN or NFV entities
• CPC Class Codes: H04L 43/02 : Capturing of monitoring data
• CPC Class Codes: H04L 43/026 : using flow identification
• CPC Class Codes: H04L 43/04 : Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 43/045 : for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/062 : related to network traffic
• CPC Class Codes: H04L 43/08 : Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 43/0805 : by checking availability
• CPC Class Codes: H04L 43/0811 : by checking connectivity
• CPC Class Codes: H04L 43/0829 : Packet loss
• CPC Class Codes: H04L 43/0841 : Round trip packet loss
• CPC Class Codes: H04L 43/0858 : One way delays
• CPC Class Codes: H04L 43/0864 : Round trip delays
• CPC Class Codes: H04L 43/0876 : Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 43/0882 : Utilisation of link capacity
• CPC Class Codes: H04L 43/0888 : Throughput
• CPC Class Codes: H04L 43/10 : Active monitoring, e.g. heartbeat, ping or trace-route
• CPC Class Codes: H04L 43/106 : using time related information in packets, e.g. by adding timestamps
• CPC Class Codes: H04L 43/12 : Network monitoring probes
• CPC Class Codes: H04L 43/16 : Threshold monitoring
• CPC Class Codes: H04L 43/20 : the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV
• CPC Class Codes: H04L 45/306 : Route determination based on the nature of the carried application
• CPC Class Codes: H04L 45/38 : Flow based routing
• CPC Class Codes: H04L 45/46 : Cluster building
• CPC Class Codes: H04L 45/507 : Label distribution
• CPC Class Codes: H04L 45/66 : Layer 2 routing, e.g. in Ethernet based MAN's
• CPC Class Codes: H04L 45/74 : Address processing for routing
• CPC Class Codes: H04L 47/11 : Identifying congestion
• CPC Class Codes: H04L 47/20 : Traffic policing
• CPC Class Codes: H04L 47/2441 : relying on flow classification, e.g. using integrated services [IntServ]
• CPC Class Codes: H04L 47/2483 : involving identification of individual flows
• CPC Class Codes: H04L 47/28 : in relation to timing considerations
• CPC Class Codes: H04L 47/31 : by tagging of packets, e.g. using discard eligibility [DE] bits
• CPC Class Codes: H04L 47/32 : by discarding or delaying data units, e.g. packets or frames
• CPC Class Codes: H04L 61/5007 : Internet protocol [IP] addresses
• CPC Class Codes: H04L 63/0227 : Filtering policies mail message filtering H04L51/212
• CPC Class Codes: H04L 63/0263 : Rule management
• CPC Class Codes: H04L 63/06 : for supporting key management in a packet data network cryptographic mechanisms or cryptographic arrangements for key management H04L9/08
• CPC Class Codes: H04L 63/0876 : based on the identity of the terminal or configuration, e.g. MAC address, hardware or software configuration or device fingerprint
• CPC Class Codes: H04L 63/1408 : by monitoring network traffic monitoring network traffic per se H04L43/00
• 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 63/145 : the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms
• CPC Class Codes: H04L 63/1458 : Denial of Service
• CPC Class Codes: H04L 63/1466 : Active attacks involving interception, injection, modification, spoofing of data unit addresses, e.g. hijacking, packet injection or TCP sequence number attacks
• CPC Class Codes: H04L 63/16 : Implementing security features at a particular protocol layer
• CPC Class Codes: H04L 63/20 : for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04L 67/01 : Protocols
• CPC Class Codes: H04L 67/10 : in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1001 : for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/51 : Discovery or management thereof, e.g. service location protocol [SLP] or web services
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/75 : Indicating network or usage conditions on the user display
• CPC Class Codes: H04L 69/16 : Implementation or adaptation of Internet protocol [IP], of transmission control protocol [TCP] or of user datagram protocol [UDP]
• CPC Class Codes: H04L 69/22 : Parsing or analysis of headers
• CPC Class Codes: H04L 7/10 : Arrangements for initial synchronisation
• CPC Class Codes: H04L 9/0866 : involving user or device identifiers, e.g. serial number, physical or biometrical information, DNA, hand-signature or measurable physical characteristics
• CPC Class Codes: H04L 9/3239 : involving non-keyed hash functions, e.g. modification detection codes [MDCs], MD5, SHA or RIPEMD
• CPC Class Codes: H04L 9/3242 : involving keyed hash functions, e.g. message authentication codes [MACs], CBC-MAC or HMAC
• CPC Class Codes: H04W 72/54 : based on quality criteria
• CPC Class Codes: H04W 84/18 : Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

An example method can include monitoring a network to identify flows between nodes in the network. Once flows have been identified, the flows can be tagged and labelled according to the type of traffic they represent. If a flow represents malicious or otherwise undesirable traffic, it can be tagged accordingly. A request can then be made for a reputation score of an entity which can identify one or more nodes of the network.

Claims

1. A computer-implemented method, comprising:

identifying a type of traffic for one or more flows associated with one or more nodes;

determining a reputation score of one or more nodes based on the type of traffic through the one or more nodes; and

blocking a node of the one or more nodes from sending traffic in response to the reputation score for that node being below a predetermined threshold;

wherein the reputation score is calculated by;

Reputation Score=Σ

_i=1ⁿValue_i+(Recovery_i*[Time_current−

Time_i])

where;

n is the number of events identified during the identifying;

Value_i is an initial relative impact of the type of traffic of an ith event on the reputation score, where Value_i is different for at least two different values of i;

Recovery_i is a constant for the type of traffic of the ith event, wherein the Recovery for at least one event is non-zero, and the recovery for at least one other event is zero;

Time_current is a time value based on a current time;

Time_i is a time value based on a time of the ith event.

2. The computer-implemented method of claim 1, further comprising, before the determining, analyzing network traffic data to classify a type of traffic for each flow of a plurality of flows.

3. The computer-implemented method of claim 2, wherein analyzing the network traffic data to classify a type of traffic for each flow includes:

matching the flow to a known pattern of traffic.

4. The computer-implemented method of claim 3, wherein the known pattern of traffic corresponds to at least one of a DDOS attack, a FIN scan, or a port knock.

5. The computer-implemented method of claim 2, wherein analyzing the network traffic data to classify a type of traffic for each flow includes:

identifying one or more network policies associated with the flow; and

determining whether the flow complies with the one or more network policies.

6. The computer-implemented method of claim 1, wherein the reputation score is associated with at least one of an endpoint, an endpoint group, a pair of endpoints, a pair of endpoint groups, an endpoint and an endpoint group, a user name for a process, a process, or a path for a process of a network.

7. The computer-implemented method of claim 1, wherein determining the reputation score includes:

determining at least one of the one or more flows is tagged as malicious or misconfigured; and

decreasing the reputation score.

8. A computer readable media storing instructions which when executed by a system cause the system to perform operations comprising:

identifying a type of traffic for one or more flows associated with one or more nodes;

determining a reputation score of one or more nodes based on the type of traffic through the one or more nodes; and

blocking a node of the one or more nodes from sending traffic in response to the reputation score for that node being below a predetermined threshold;

wherein the reputation score is calculated by;

Reputation Score=Σ

_i=1ⁿValue_i+(Recovery_i*[Time_current−

Time_i])

where;

n is the number of events identified during the identifying;

Value_i is an initial relative impact of the type of traffic of an ith event on the reputation score, where Value_i is different for at least two different values of i;

Recovery_i is a constant for the type of traffic of the ith event, wherein the Recovery for at least one event is non-zero, and the recovery for at least one other event is zero;

Time_current is a time value based on a current time;

Time_i is a time value based on a time of the ith event.

9. The media of claim 8, the operations further comprising, before the determining, analyzing network traffic data to classify a type of traffic for each flow of a plurality of flows.

10. The media of claim 9, wherein analyzing network traffic data to classify a type of traffic for each flow includes:

matching the flow to a known pattern of traffic.

11. The media of claim 10, wherein the known pattern of traffic corresponds to at least one of a DDOS attack, a FIN scan, or a port knock.

12. The media of claim 8, wherein analyzing network traffic data to classify a type of traffic for each flow includes:

identifying one or more network policies associated with the flow; and

determining whether the flow complies with the one or more network policies.

13. The media of claim 8, wherein the reputation score is associated with at least one of an endpoint, an endpoint group, a pair of endpoints, a pair of endpoint groups, an endpoint and an endpoint group, a user name for a process, a process, or a path for a process of a network.

14. The media of claim 8, wherein determining the reputation score includes:

determining at least one of the one or more flows is tagged as malicious or misconfigured; and

decreasing the reputation score.

15. A system, comprising:

a computer readable media storing instructions;

a processor programmed to execute the instructions to perform operations comprising;

identifying a type of traffic for one or more flows associated with one or more;

determining a reputation score of one or more nodes based on the type of traffic through the one or more nodes; and

blocking a node of the one or more nodes from sending traffic in response to the reputation score for that node being below a predetermined threshold;

wherein the reputation score is calculated by;

Reputation Score=Σ

_i=1ⁿValue_i+(Recovery_i*[Time_current−

Time_i])

where;

n is the number of events identified during the identifying;

Value_i is an initial relative impact of the type of traffic of an ith event on the reputation score, where Value_i is different for at least two different values of i;

Recovery_i is a constant for the type of traffic of the ith event, wherein the Recovery for at least one event is non-zero, and the recovery for at least one other event is zero;

Time_current is a time value based on a current time;

Time_i is a time value based on a time of the ith event.

16. The system of claim 15, the operations further comprising, before the determining, analyzing network traffic data to classify a type of traffic for each flow of a plurality of flows.

17. The system of claim 16, wherein analyzing the network traffic data to classify a type of traffic for each flow includes:

matching the flow to a known pattern of traffic.

18. The system of claim 17, wherein the known pattern of traffic corresponds to at least one of a DDOS attack, a FIN scan, or a port knock.

19. The system of claim 16, wherein analyzing the network traffic data to classify a type of traffic for each flow includes:

identifying one or more network policies associated with the flow; and

determining whether the flow complies with the one or more network policies.

20. The system of claim 15, wherein the reputation score is associated with at least one of an endpoint, an endpoint group, a pair of endpoints, a pair of endpoint groups, an endpoint and an endpoint group, a user name for a process, a process, or a path for a process of a network.