TECHNOLOGIES FOR DETERMINING SENSOR PLACEMENT AND TOPOLOGY

• US 20160359703A1
• Filed: 05/11/2016
• Published: 12/08/2016
• 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: Gandham, Shashidhar, Prasad, Rohit Chandra, Kulshreshtha, Ashutosh, Rao, Supreeth Hosur Nagesh, Deen, Khawar, Yadav, Navindra
• Granted Patent: US 9,935,851 B2
• 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 for determining sensor placement and topology. In some embodiments, a system can receive messages from sensors deployed around a network, each of the messages reporting a respective flow captured by a reporting sensor from the sensors. Next, the system can identify flows reported in the messages and, for each of the flows, generate a respective list of sensors that reported that flow. Based on the respective list of sensors, the system can infer a respective placement of the sensors within the network and a topology of the sensors. For example, the system can determine that a first sensor is deployed in a virtual machine, a second sensor is deployed in a hypervisor hosting the virtual machine, and a third sensor is deployed in a network device configured to route traffic associated with the hypervisor.

Claims

1. A method comprising:

receiving, by a computing device, messages from sensors deployed around a network, each of the messages reporting a respective flow captured by a reporting sensor from the sensors;

identifying flows reported in the messages;

for each of the flows, generating a respective list of sensors that reported that flow; and

based on the respective list of sensors, inferring at least one of a respective placement of the sensors within the network and a topology of the sensors.

2. The method of claim 1, wherein the sensors comprise a first sensor, a second sensor, and a third sensor, and wherein the sensors are respectively deployed in one of a virtual machine, a hypervisor hosting the virtual machine, or a network device configured to route traffic associated with the hypervisor, and wherein the inferring step comprises determining that the first sensor is deployed in the virtual machine, the second sensor is deployed in the hypervisor, and the third sensor is deployed in the network device.

3. The method of claim 2, wherein the determining is based on:

a first determination that a first flow was reported by the first sensor, the second sensor, and the third sensor; and

a second determination that a second flow was not reported by the first sensor.

4. The method of claim 3, wherein the determining is further based on a third determination that a third flow was only reported by the third sensor.

5. The method of claim 1, wherein each of the messages comprises a sensor identifier associated with the respective flow, and wherein generating the list is based on the sensor identifier associated with each respective flow.

6. The method of claim 1, wherein each of the sensors is deployed at a respective hop within a network path, and wherein each of the sensors is configured to report, to the computing device, each flow observed at the respective hop.

7. The method of claim 6, wherein the network path comprises a plurality of hops, the plurality of hops comprising a virtual machine, a hypervisor hosting the virtual machine, and a network device configured to route traffic associated with the hypervisor.

8. The method of claim 7, wherein the plurality of hops further comprises a server hosting the hypervisor and virtual machine.

9. The method of claim 7, wherein the inferring step comprises determining which of the sensors is deployed at the virtual machine, which of the sensors is deployed at the hypervisor, and which of the sensors is deployed at the network device.

10. The method of claim 1, wherein each message comprises a list of one or more network flows captured and reported by a respective sensor.

11. The method of claim 1, wherein the generating and inferring steps comprise:

generating a plurality of respective lists of sensors for the flows, each of the plurality of respective lists of sensors identifying one or more respective sensors reporting a respective one of the flows;

for each specific sensor in each respective list of sensors from the plurality of respective lists of sensors;

identifying a respective set of other listed sensors comprising all sensors in the respective list of sensors excluding the specific sensor;

generating a list of respective sets of other listed sensors, the list of respective sets comprising each respective set of other listed sensors identified for the specific sensor;

determining a respective union of sensors in the list of respective sets;

determining a respective intersection of sensors in the list of respective sets;

computing a respective difference between the respective union and the respective intersection;

identifying one or more front sensors corresponding to the respective intersection of sensors, the one or more front sensors comprising a first set of all sensors that can observe all flows from the specific sensor; and

identifying one or more rear sensors corresponding to the respective difference, the one or more rear sensors comprising a second set of all sensors whose flows can also be observed by the specific sensor.

12. The method of claim 11, wherein inferring the at least one of the respective placement of the sensors within the network and the topology of the sensors is based on the one or more rear sensors and one or more front sensors identified for each specific sensor.

13. The method of claim 11, further comprising:

when the respective intersection contains multiple sensors and the respective difference is zero or empty, determining that the specific sensor is deployed on a virtual machine;

when the respective intersection contains one sensor and the respective difference is a different sensor, determining that the specific sensor is deployed on a hypervisor hosting the virtual machine; and

when the respective intersection contains zero sensors and the respective difference is zero, determining that the specific sensor is deployed on a network device configured to route traffic associated with the hypervisor.

14. The method of claim 1, wherein:

at least one of the sensors comprises at least one of a first process, a first kernel module, or a first kernel driver on a guest operating system installed on a virtual machine;

at least one sensor comprises at least one of a second process, a second kernel module, or a second kernel driver on a host operating system installed at a hypervisor layer; and

at least one sensor comprises at least one of a third process or a software agent running on a network device.

15. A system comprising:

a processor; and

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

receiving messages from sensors deployed around a network, each of the messages reporting a respective flow captured by a reporting sensor from the sensors;

identifying flows reported in the messages;

for each of the flows, generating a respective list of sensors that reported that flow; and

based on the respective list of sensors, inferring at least one of a respective placement of the sensors within the network and a topology of the sensors.

16. The system of claim 15, wherein the generating and inferring steps comprise:

generating a plurality of respective lists of sensors for the flows, each of the plurality of respective lists of sensors identifying one or more respective sensors reporting an associated flow;

for each specific sensor in each respective list of sensors from the plurality of respective lists of sensors, identifying a respective set of other listed sensors comprising all sensors in the respective list of sensors excluding the specific sensor;

for each specific sensor, generating a list of respective sets of other listed sensors, the list of respective sets comprising each respective set of other listed sensors identified for the specific sensor;

for each specific sensor;

determining a respective union of sensors in the list of respective sets;

determining a respective intersection of sensors in the list of respective sets;

computing a respective difference between the respective union and the respective intersection;

identifying one or more front sensors corresponding to the respective intersection of sensors, the one or more front sensors comprising a first set of all sensors that can observe all flows from the specific sensor; and

identifying one or more rear sensors corresponding to the respective difference, the one or more rear sensors comprising a second set of all sensors whose flows can also be observed by the specific sensor.

17. The system of claim 16, wherein inferring the at least one of the respective placement of the sensors within the network and the topology of the sensors is based on the one or more rear sensors and one or more front sensors identified for each specific sensor.

18. The system of claim 17, wherein the sensors comprise a first sensor, a second sensor, and a third sensor, and wherein the sensors are respectively deployed in one of a virtual machine, a hypervisor hosting the virtual machine, or a network device configured to route traffic associated with the hypervisor, and wherein the inferring the at least one of the respective placement of the sensors within the network and the topology of the sensors comprises determining that the first sensor is deployed in the virtual machine, the second sensor is deployed in the hypervisor, and the third sensor is deployed in the network device.

19. The system of claim 15, wherein each of the sensors is deployed at a respective hop within a network path, and wherein each of the sensors is configured to report, to the system, each flow observed at the respective hop, wherein the network path comprises a plurality of hops, the plurality of hops comprising a virtual machine, a hypervisor hosting the virtual machine, and a network device configured to route traffic associated with the hypervisor.

20. A non-transitory computer-readable storage medium having stored therein instructions which, when executed by a processor, cause the processor to perform operations comprising:

receiving, by a computing device, messages from sensors deployed around a network, each of the messages reporting a respective flow captured by a reporting sensor from the sensors;

identifying flows reported in the messages;

generating a plurality of respective lists of sensors for the flows, each of the plurality of respective lists of sensors identifying one or more respective sensors reporting a respective one of the flows; and

based on the plurality of respective lists of sensors, inferring at least one of a respective placement of the sensors within the network and a topology of the sensors by;

for each specific sensor in each respective list of sensors from the plurality of respective lists of sensors;

identifying a respective set of other listed sensors comprising all sensors in the respective list of sensors excluding the specific sensor;

generating a list of respective sets of other listed sensors, the list of respective sets comprising each respective set of other listed sensors identified for the specific sensor;

determining a respective union of sensors in the list of respective sets;

determining a respective intersection of sensors in the list of respective sets;

computing a respective difference between the respective union and the respective intersection;

identifying one or more front sensors corresponding to the respective intersection of sensors, the one or more front sensors comprising a first set of all sensors that can observe all flows from the specific sensor; and

identifying one or more rear sensors corresponding to the respective difference, the one or more rear sensors comprising a second set of all sensors whose flows can also be observed by the specific sensor.