NETWORK FLOW DE-DUPLICATION

• US 20160359759A1
• Filed: 05/24/2016
• Published: 12/08/2016
• Est. Priority Date: 06/05/2015
• Status: Abandoned Application

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Singh, Abhishek Ranjan, Chang, Shih-Chun, Malhotra, Varun Sagar, Vu, Hai Trong, Pang, Jackson Ngoc Ki, Gupta, Anubhav
• Application Number:
  US15/163,612
• Publication Number:
  US 20160359759A1
• Time in Patent Office: Days
• US Class Current: 1/1
• 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
• CPC Class Codes: G06F 16/173 Customisation support for file systems, e.g. localisation, multi-language support, personalisation
• 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

Systems, methods, and computer-readable media are provided for de-duplicating sensed data packets in a network. As data packets of a particular network flow move through the network, the data packets can be sensed and reported by various sensors across the network. An optimal sensor of the network can be determined based upon data packets reported by the various sensors. Data packets sensed and reported by the optimal sensor can be preserved for network analysis. Duplicative data packets of the particular network flow sensed and reported by other sensors of the network can be discarded to save storage capacity and processing power of network-flow analysis tools. Analysis of the particular network flow can be performed based upon the data packets sensed by the optimal sensor and non-duplicative data packets of the particular network-flow sensed by other sensors of the network.

Claims

1. A method comprising:

receiving, from a plurality of sensors in a network, data packets of a particular network flow of the network;

analyzing the data packets to determine a specific sensor of the plurality of sensors;

preserving data packets sensed and reported from the specific sensor;

determining duplicative data packets sensed and reported from other sensor(s) of the plurality of sensors based upon the data packets reported from the specific sensor;

discarding the duplicative data packets sensed and reported from the other sensor(s); and

analyzing the particular network flow based upon the data packets reported from the specific sensor and non-duplicative data packets reported from the other sensor(s).

2. The method of claim 1, further comprising:

analyzing the data packets from the plurality of sensors to determine a number of data packets sensed by each sensor of the plurality of sensors,

wherein the specific sensor has sensed the most number of data packets of the particular network flow among the plurality of sensors.

3. The method of claim 1, wherein the receiving, from the plurality of sensors, the data packets of the particular network flow comprises:

receiving, from the plurality of sensors, data packets of the particular network flow for a predetermined time period,

wherein the specific sensor is determined based upon the data packets of the particular network flow sensed during the predetermined time period.

4. The method of claim 1, wherein the receiving, from the plurality of sensors, the data packets of the particular network flow further comprises:

sampling the data packets of the particular network flow received from the plurality of sensors,

wherein the specific sensor is determined based upon sampled data packets of the particular network flow.

5. The method of claim 1, further comprising:

reconciling the data packets of the particular network flow received from the plurality of sensors; and

determining non-duplicative data packets of the particular network flow,

wherein the specific sensor has sensed the most number of the non-duplicative data packets of the particular network flow among the plurality of sensors.

6. The method of claim 1, wherein the data packets of the particular network flow comprise a set of information to uniquely identify the particular network flow, the set of information including a source address, a destination address, a source port, destination port, a protocol, a user identification (ID), and a starting timestamp.

7. The method of claim 6, further comprising:

analyzing the data packets from the plurality of sensors to determine a starting timestamp for each of the data packets,

wherein the specific sensor sensed the earliest data packet of the particular network flow.

8. The method of claim 1, wherein the particular network flow is a first user datagram protocol (UDP) network flow, further comprising:

determining that the first UDP network flow being inactive for a predetermined time period; and

using a new flow start-time to distinguish a second UDP network flow from the first UDP network flow.

9. The method of claim 1, wherein the particular network flow is a transmission control protocol (TCP) network flow, further comprising:

determining a start of the TCP network flow based upon a three-way hand-shake.

10. The method of claim 8, further comprising:

determining an end of the TCP network flow based upon a four-way hand-shake.

11. A system comprising:

a processor; and

a computer-readable storage medium storing instructions which, when executed by the processor, cause the system to perform operations comprising;

receiving, from a plurality of sensors in a network, data packets of a particular network flow of the network;

analyzing the data packets to determine a specific sensor of the plurality of sensors;

preserving data packets sensed and reported from the specific sensor;

determining duplicative data packets sensed and reported from other sensor(s) of the plurality of sensors based upon the data packets reported from the specific sensor;

discarding the duplicative data packets sensed and reported from the other sensor(s); and

analyzing the particular network flow based upon the data packets reported from the specific sensor and non-duplicative data packets reported from the other sensor(s).

12. The system of claim 11, wherein the instructions, when executed by the processor, cause the system to perform operations further comprising:

analyzing the data packets from the plurality of sensors to determine a number of data packets sensed by each sensor of the plurality of sensors,

wherein the specific sensor has sensed the most number of data packets of the particular network flow among the plurality of sensors.

13. The system of claim 11, wherein the instructions, when executed by the processor, cause the system to perform operations further comprising:

receiving, from the plurality of sensors, data packets of the particular network flow for a predetermined time period,

wherein the specific sensor is determined based upon the data packets of the particular network flow sensed during the predetermined time period.

14. The system of claim 11, wherein the instructions, when executed by the processor, cause the system to perform operations further comprising:

sampling the data packets of the particular network flow received from the plurality of sensors,

wherein the specific sensor is determined based upon sampled data packets of the particular network flow.

15. The system of claim 11, wherein the instructions, when executed by the processor, cause the system to perform operations further comprising:

reconciling the data packets of the particular network flow received from the plurality of sensors; and

determining non-duplicative data packets of the particular network flow,

wherein the specific sensor has sensed the most number of the non-duplicative data packets of the particular network flow among the plurality of sensors

16. The system of claim 11, wherein the data packets of the particular network flow comprise a set of information to uniquely identify the particular network flow, the set of information including a source address, a destination address, a source port, destination port, a protocol, a user identification (ID), and a starting timestamp, and wherein the instructions, when executed by the processor, cause the system to perform operations further comprising:

analyzing the data packets from the plurality of sensors to determine a starting timestamp for each of the data packets,

wherein the specific sensor sensed the earliest data packet of the particular network flow.

17. The system of claim 11, wherein the particular network flow is a transmission control protocol (TCP) network flow, and wherein the instructions, when executed by the processor, cause the system to perform operations further comprising:

determining a start of the TCP network flow based upon a three-way hand-shake; and

determining an end of the TCP network flow based upon a four-way hand-shake.

18. A non-transitory computer-readable storage medium storing instructions for de-duplicating sensed data packets in a network, that, when executed by at least one processor of a computing system, cause the computing system to perform operations comprising:

receiving, from a plurality of sensors in the network, data packets of a particular network flow of the network;

analyzing the data packets to determine a specific sensor of the plurality of sensors;

preserving data packets sensed and reported from the specific sensor;

determining duplicative data packets sensed and reported from other sensor(s) of the plurality of sensors based upon the data packets reported from the specific sensor;

discarding the duplicative data packets sensed and reported from the other sensor(s); and

analyzing the particular network flow based upon the data packets reported from the specific sensor and non-duplicative data packets reported from the other sensor(s).

19. The non-transitory computer-readable storage medium of claim 18, wherein the instructions, when executed by the at least one processor, cause the computing system to perform operations further comprising:

analyzing the data packets from the plurality of sensors to determine a number of data packets sensed by each sensor of the plurality of sensors,

wherein the specific sensor has sensed the most number of data packets of the particular network flow among the plurality of sensors.

20. The non-transitory computer-readable storage medium of claim 18, wherein the data packets of the particular network flow comprise a set of information to uniquely identify the particular network flow, the set of information including a source address, a destination address, a source port, destination port, a protocol, a user identification (ID), and a starting timestamp, and wherein the instructions, when executed by the at least one processor, cause the computing system to perform operations further comprising:

analyzing the data packets from the plurality of sensors to determine a starting timestamp for each of the data packets,

wherein the specific sensor sensed the earliest data packet of the particular network flow.