Intra-datacenter attack detection

• US 10,567,247 B2
• Filed: 05/03/2016
• Issued: 02/18/2020
• 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: Kulshreshtha, Ashutosh, Rao, Supreeth Hosur Nagesh, Yadav, Navindra, Gupta, Anubhav, Gupta, Sunil Kumar, Malhorta, Varun Sagar, Gandham, Shashidhar
• Primary Examiner(s): Gracia, Gary S
• Application Number:
  US15/145,630
• Publication Number:
  US 20160359877A1
• Time in Patent Office: 1,386 Days
• Field of Search: 718 1, 726 11, 726 22, 726 23, 726 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
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• 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 receiving a traffic report from a sensor and using the traffic report to detect intra-datacenter flows. These intra-datacenter flows can then be compared with a description of historical flows. The description of historical flows can identify characteristics of normal and malicious flows. Based on the comparison, the flows can be classified and tagged as normal, malicious, or anomalous. If the flows are tagged as malicious or anomalous, corrective action can be taken with respect to the flows. A description of the flows can then be added to the description of historical flows.

Claims

1. A computer-implemented method, comprising:

capturing, by a datacenter analytics module, traffic that includes intra-datacenter flows and extra-datacenter flows;

identifying with the captured traffic a subset of the intra-datacenter flows;

comparing, by the datacenter analytics module, the subset of the intra-datacenter flows with historical intra-datacenter-data flows, the comparing comprising an iterative comparison utilizing an increasing amount of granularity;

determining, by the datacenter analytics module, that the subset of the intra-datacenter flows corresponds to anomalous traffic based on the comparison;

analyzing, by the datacenter analytics module, the subset of the intra-datacenter flows to determine whether the subset of the intra-datacenter flows corresponds to a malicious attack; and

dropping the subset of the intra-datacenter flows in response to the analyzing making a determination that the subset of the intra-datacenter flows corresponds to a malicious attack.

2. The computer-implemented method of claim 1, wherein the comparing includes:

determining a number of the subset of the intra-datacenter flows that include unique source hosts communicating with a common destination host.

3. The computer-implemented method of claim 1, wherein obtaining the comparing includes:

determining a number of the subset of the intra-datacenter flows that include unique ports.

4. The computer-implemented method of claim 1, wherein the subset of the intra-datacenter flows and the historical intra-datacenter-data flows are limited to flows between one source host and one destination host.

5. The computer-implemented method of claim 1, wherein determining that the subset of the intra-datacenter flows corresponds to anomalous traffic includes:

determining a reputation score associated with a host corresponding to one of the subset of the intra-datacenter flows.

6. The computer-implemented method of claim 1, wherein capturing the subset of the intra-datacenter flows includes:

receiving a traffic report that includes flow data from a first sensor installed on a host, a second sensor installed on a hypervisor, and a third sensor installed on a switch,

wherein each of the first sensor, the second sensor, and the third sensor respectively report on packets sent from or through the host, the hypervisor, and the switch.

7. The computer-implemented method of claim 1, further comprising:

capturing a subset of the extra-datacenter flows;

determining that the subset of the intra-datacenter flows originates from one or more attacking hosts;

determining, based on the subset of the extra-datacenter flows, that each of the one or more attacking hosts received a respective extra-datacenter flow of the subset of the extra-datacenter flows from a common host before initiating a respective intra-datacenter flow; and

identifying the common host as an extra-datacenter flow control host by correlating each of the respective extra-datacenter flow with the common host.

8. A non-transitory computer-readable medium comprising instructions stored thereon, the instructions, when executed, cause a computing device, which analyzes intra-datacenter flows and extra-datacenter flows, to perform operations comprising:

capture traffic that includes intra-datacenter flows and extra-datacenter flows;

identify with the captured traffic a subset of the intra-datacenter flows;

compare the subset of the intra-datacenter flows with historical intra-datacenter-data flows, the compare comprising an iterative comparison utilizing an increasing amount of granularity;

determine that the subset of the intra-datacenter flows corresponds to anomalous traffic based on the comparison;

analyze the subset of the intra-datacenter flows to determine whether the subset of the intra-datacenter flows corresponds to a malicious attack; and

dropping the subset of the intra-datacenter flows in response to the analyze making a determination that the subset of the intra-datacenter flows corresponds to a malicious attack.

9. The non-transitory computer-readable medium of claim 8, wherein the instructions, when executed to cause the computing device to obtain the comparison, include causing the computing device to:

determine a number of the subset of the intra-datacenter flows that include unique source hosts communicating to a common destination host.

10. The non-transitory computer-readable medium of claim 8, wherein the instructions, when executed to cause the computing device to obtain the comparison, include causing the computing device to:

determine a number of the subset of the intra-datacenter flows that include unique ports.

11. The non-transitory computer-readable medium of claim 8, wherein the subset of the intra-datacenter flows and the historical intra-datacenter-data flows are limited to flows between one source host and one destination host.

12. The non-transitory computer-readable medium of claim 8, wherein the instructions, when executed to cause the computing device to determine that the subset of the intra-datacenter flow corresponds to anomalous traffic, include causing the computing device to:

determine a reputation score associated with a host corresponding to one of the subset of the intra-datacenter flows.

13. The non-transitory computer-readable medium of claim 8, wherein the instructions, when executed to cause the computing device to capture the subset of the intra-datacenter flows, include causing the computing device to:

receive a traffic report that includes flow data from a first sensor installed on a host, a second sensor installed on a hypervisor, and a third sensor installed on a switch,

wherein each of the first sensor, the second sensor, and the third sensor respectively report on packets sent from or through the host, the hypervisor, and the switch.

14. The non-transitory computer-readable medium of claim 8, wherein the instructions when executed further cause the computing device to:

capture a subset of the extra-datacenter flows;

determine that the subset of the intra-datacenter flows originates from one or more attacking hosts;

determine, based on the subset of the extra-datacenter flows, that each of the one or more attacking hosts received a respective extra-datacenter flow of the subset of the extra-datacenter flows from a common host before initiating a respective intra-datacenter flow; and

identify the common host as an extra-datacenter flow control host by correlating each of the respective extra-datacenter flow with the common host.

15. A system, comprising:

a non-transitory computer readable memory storing instructions;

a processor programmed to cooperate with the instructions to perform operations comprising;

capturing, by a datacenter analytics module, traffic that includes intra-datacenter flows and extra-datacenter flows;

identifying with the captured traffic a subset of the intra-datacenter flows;

comparing, by the datacenter analytics module, the subset of the intra-datacenter flows with historical intra-datacenter-data flows, the comparing comprising an iterative comparison utilizing an increasing amount of granularity;

determining, by the datacenter analytics module, that the subset of the intra-datacenter flows corresponds to anomalous traffic based on the comparison;

analyzing, by the datacenter analytics module, the subset of the intra-datacenter flows to determine whether the subset of the intra-datacenter flows corresponds to a malicious attack; and

dropping the subset of the intra-datacenter flows in response to the analyzing making a determination that the subset of the intra-datacenter flows corresponds to a malicious attack.

16. The system of claim 15, wherein the comparing includes:

determining a number of the subset of the intra-datacenter flows that include unique source hosts communicating with a common destination host.

17. The system of claim 15, wherein obtaining the comparing includes:

determining a number of the subset of the intra-datacenter flows that include unique ports.

18. The system of claim 15, wherein the subset of the intra-datacenter flows and the historical intra-datacenter-data flows are limited to flows between one source host and one destination host.

19. The system of claim 15, wherein capturing the subset of the intra-datacenter flows includes:

receiving a traffic report that includes flow data from a first sensor installed on a host, a second sensor installed on a hypervisor, and a third sensor installed on a switch,

wherein each of the first sensor, the second sensor, and the third sensor respectively report on packets sent from or through the host, the hypervisor, and the switch.

20. The system of claim 15, further comprising:

capturing a subset of the extra-datacenter flows;

determining that the subset of the intra-datacenter flows originates from one or more attacking hosts;

determining, based on the subset of the extra-datacenter flows, that each of the one or more attacking hosts received a respective extra-datacenter flow of the subset of the extra-datacenter flows from a common host before initiating a respective intra-datacenter flow; and

identifying the common host as an extra-datacenter flow control host by correlating each of the respective extra-datacenter flow with the common host.