SERVER-CLIENT DETERMINATION

• US 20160359686A1
• Filed: 04/27/2016
• 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: Parandehgheibi, Ali, Singh, Abhishek Ranjan, Madani, Omid, Jeyakumar, Vimalkumar, Scheib, Ellen Christine, Yadav, Navindra, Alizadeh Attar, Mohammadreza
• Granted Patent: US 10,505,827 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 are provided for determining whether a node in a network is a server or a client. In some examples, a system can collect, from one or more sensors that monitor at least part of data traffic being transmitted via a pair of nodes in a network, information of the data traffic. The system can analyze attributes of the data traffic such as timing, port magnitude, degree of communication, historical data, etc. Based on analysis results and a predetermined rule associated with the attributes, the system can determine which node of the pair of nodes is a client and which node is a server.

Claims

1. A method comprising:

receiving, from a first sensor that monitors at least part of first data traffic exchanged between a pair of nodes in a network, first information of the first data traffic, the pair of nodes comprising a first node and a second node;

determining, by analyzing the first information, that the first node initiated the first data traffic; and

determining that the first node is a client and that the second node is a server.

2. The method of claim 1, wherein the first sensor is located at one of the first node, the second node, or a network node different from the first node and the second node, the network node being at least one of a server, a virtual machine, a hypervisor, a switch, or a gateway.

3. The method of claim 1, further comprising:

receiving, from a second sensor that monitors at least part of the first data traffic exchanged between the pair of nodes, second information of the first data traffic,

wherein the determining that the first node initiated the first data traffic is further based on the second information.

4. The method of claim 3, wherein each of the first information and the second information is weighted with a respective weight for the estimation.

5. The method of claim 4, wherein the respective weight is determined based on at least one of a length of time, an amount of information, or a margin of error, associated with monitoring the first data traffic by a corresponding sensor.

6. The method of claim 1, further comprising:

receiving, from a plurality of sensors that respectively monitor at least part of the first data traffic exchanged between the pair of nodes, respective information of the first data traffic,

wherein the determining that the first node initiated the first data traffic is further based on the respective information.

7. The method of claim 1, further comprising:

generating a communication graph comprising the first node, the second node, and a third node in the network;

receiving, from a second sensor that monitors at least part of second data traffic exchanged between the second node and the third node, second information of the second data traffic;

determining, based upon the second information, that the second node has initiated the second data traffic exchange between the second node and the third node; and

determining that the second node is the client and the third node is another server.

8. The method of claim 1, further comprising:

receiving second data traffic exchanged between one or more known client-server pairs in the network;

performing a series of machine learning tasks based at least upon the second data traffic data exchanged between the one or more known client-server pairs; and

determining client-server statuses of other nodes in the network using the series of machine learning tasks.

9. The method of claim 8, wherein the series of machine learning tasks comprise a supervised learning, and wherein the supervised learning is used to determine that the first node initiated the first data traffic.

10. The method of claim 9, wherein the series of machine learning tasks comprise at least one machine learning task from a decision tree, a random forest, a neural network, or a random forest.

11. A method comprising:

receiving, from a first sensor that monitors at least part of first data traffic being transmitted via a pair of nodes in a network, first information of the first data traffic, the pair of nodes comprising a first node and a second node;

determining, by analyzing the first information, a first degree of connection for the first node and a second degree of connection for the second node, wherein the first degree of connection indicates how many unique address-port pairs that the first node has communicated with, and wherein the second degree of connection indicates how many unique address-port pairs that the second node has communicated with;

determining that the first degree of connection is greater than the second degree of connection; and

determining that the first node is a server and that the second node is a client.

12. The method of claim 11, wherein each of the unique address-port pairs comprises an Internet protocol address and a port number.

13. The method of claim 11, wherein the first sensor is located at one of the first node, the second node, or a network node different from the first node and the second node, the network node being at least one of a server, a virtual machine, a hypervisor, a switch, or a gateway.

14. The method of claim 11, further comprising:

generating a communication graph comprising the first node, the second node, and a third node in the network;

receiving, from a second sensor that monitors at least part of second data traffic exchanged between the second node and the third node, second information of the second data traffic;

determining, based upon the second information, that the second node has initiated the second data traffic exchange between the second node and the third node; and

determining that the second node is the client and the third node is another server.

15. The method of claim 14, further comprising:

receiving second data traffic exchanged between one or more known client-server pairs in the network;

performing a series of machine learning tasks based at least upon the second data traffic data exchanged between the one or more known client-server pairs; and

determining client-server statuses of other nodes in the network based upon the series of machine learning tasks.

16. The method of claim 15, wherein the series of machine learning tasks comprise a supervised learning, and wherein the first degree of connection and the second degree of connection are determined using the supervised learning.

17. The method of claim 16, wherein the series of machine learning tasks comprise at least one machine learning task from a decision tree, a random forest, a neural network, or a random forest.

18. 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 first sensor that monitors at least part of first data traffic being transmitted via a pair of nodes in a network, first information of the first data traffic, the pair of nodes comprising a first node and a second node;

determining, by analyzing the first information, timings of first data traffic being transmitted via the pair of nodes and degrees of connection for the pair of nodes, the first node having a first degree of connection and the second node having a second degree of connection; and

determining, based at least upon the timings of first data traffic, the degrees of connection, and a predetermined rule associated with the timings of the first data traffic and the degrees of connections, that the first node initiated the first data traffic or the first degree of connection is less than the second degree of connection; and

determining that the first node is a client and that the second node is a server.

19. The system of claim 18, wherein the instructions, when executed by the processor, cause the system to perform operations further comprising:

receiving, from a second sensor that monitors at least part of the first data traffic exchanged between the pair of nodes, second information of the first data traffic,

wherein the determining the timings of first data traffic and the degrees of connection is further based on the second information.

20. The system of claim 18, wherein the instructions, when executed by the processor, cause the system to perform operations further comprising:

generating a communication graph comprising the first node, the second node, and a third node in the network;

receiving, from a second sensor that monitors at least part of second data traffic exchanged between the second node and the third node, second information of the second data traffic;

determining, based upon the second information, that the second node has initiated the second data traffic exchange between the second node and the third node; and

determining that the second node is the client and the third node is another server.