Vmware, Inc.

Nicira, Inc.

Margaret Angeline Petrus

Aditi Vutukuri

Mallik Mahalingam

Thayumanavan Sridhar

Chidambareswaran Raman

Anuprem Chalvadi

Tejas Sanjeev Panse

Rishi Kanth Alapati

Parasuramji Rajendran

Weiming Xu

Shireesh Kumar Singh

Rajiv Mordani

Sunitha Krishna

Arnold Koon-Chee Poon

Bin Wang

Karen Hayrapetyan

Nikash Walia

Pallavi Moghe

Mengzhuo Lu

Santhanakrishnan Kaliya Perumal

Rajiv Krishnamurthy

Laxmikant Vithal Gunda

Farzad Ghannadian

Russell Lu

Radha Popuri

Ming Wen

Yiwei Zhang

Pradeep Kankipati

Kavya Kambi Ravi

Lan Luo

Edilmo Palencia

Systems and methods are described for recommending security groups using graph-based learning models. A server can create a network graph that illustrates network flows between devices in a network and security groups that the devices belong to. The network graph can include nodes that represent the devices and security groups. The server can apply a graph-based learning model to learn embeddings of the nodes and create vectors using the embeddings. Using vectors of two nodes, the server can calculate a vector that represents an edge between the two nodes. The server can apply a binary classifier determine whether the edge should exist. A 'true' classification between two nodes can indicate that they should be able to communicate, and vice versa. A “true” classification between a device node and a security group node can indicate that the device should be assigned to the security group, and vice versa.

Modifying network relationships using a heterogenous network flows graph

Some embodiments provide a method for modifying a firewall rule of a security policy implemented in a network. The method identifies a set of compute machines to be added to a match condition for the firewall rule. The match condition is expressed using one or more groups of compute machines. The method selects a set of groups for the identified set of compute machines from a plurality of existing groups of compute machines based on a user-specified threshold indicating tolerance for inclusion of compute machines that are not in the identified set of compute machines in the selected groups. The method uses the selected set of groups for the match condition of the firewall rule.

Reuse of groups in security policy

Some embodiments provide a method for detecting a threat to a datacenter. The method receives a set of connections between a set of DCNs in the datacenter over a particular time period. The set of DCNs includes at least a first DCN at which a first anomalous event was detected. The method analyzes a set of detected anomalous events to identify additional anomalous events detected at other DCNs in the set of DCNs during the particular time period. Based on the first anomalous event and identified additional anomalous events, the method determines whether the anomalous events indicate a threat to the datacenter.

Detecting threats to datacenter based on analysis of anomalous events

Some embodiments provide a method for identifying security threats to a datacenter. From multiple host computers in the datacenter, the method receives attribute sets for multiple flows. Each respective attribute set for a respective flow includes at least (i) a source identifier for the respective flow and (ii) an indicator as to whether the respective flow is indicative of the source of the respective flow being a security threat. For each of multiple source identifiers, the method aggregates the received attribute sets to generate an aggregate attribute set for the source identifier that includes a combined measurement of security threat indicators. For a particular source identifier, the method adjusts a security threat likelihood score for the source corresponding to the particular source identifier based on the combined measurement of security threat indicators for the source identifier.

Security threat detection based on network flow analysis

Example methods and systems for self-learning packet flow monitoring. One example method may comprise monitoring a packet flow to identify attribute information associated with the packet flow between a source and a destination; and classifying the packet flow using a classification engine that is trained using a training dataset to determine a classification output associated with the packet flow. The example method may also comprise providing, to a user device, a user interface (UI) that includes a visualization of the packet flow and the classification output; requesting, via the UI, a feedback associated with the classification output for the packet flow; and updating the classification engine or the training dataset based on the feedback from the user device.

Self-learning packet flow monitoring in software-defined networking environments

Some embodiments provide a method for detecting a threat to a datacenter. The method generates a graph of connections between data compute nodes (DCNs) in the datacenter. Each connection has an associated time period during which the connection is active. The method receives an anomalous event occurring during a particular time period at a particular DCN operating in the datacenter. The method analyzes the generated graph to determine a set of paths between DCNs in the datacenter that include connections to the particular DCN during the particular time period. The method uses the set of paths to identify a threat to the datacenter.

Identification of time-ordered sets of connections to identify threats to a datacenter

Some embodiments provide a method for identifying security threats to a datacenter. The method receives flow attribute sets for multiple flows from multiple host computers in the datacenter on which data compute nodes (DCNs) execute. Each flow attribute set indicates at least a source DCN for the flow. The method identifies flow attribute sets that correspond to DCNs responding to name resolution requests. For each DCN of a set of DCNs executing on the host computers, the method determines whether the DCN has sent responses to name resolution requests in a manner that deviates from a historical baseline for the DCN based on the identified flow attribute sets. When a particular DCN has sent responses to name resolution requests in a manner that deviates from a historical baseline for the particular DCN, the method identifies the particular DCN as a security threat to the datacenter.

Detection of threats based on responses to name resolution requests

Some embodiments provide a method for identifying security threats to a datacenter. From multiple host computers in the datacenter, the method receives data indicating port usage for a particular time period for each of multiple destination data compute nodes (DCNs) executing on the host computers. For each DCN of a set of the destination DCNs, identifies whether the port usage for the particular time period deviates from a historical baseline port usage for the DCN. When the port usage for a particular DCN deviates from the historical baseline for the particular DCN, the method identifies the particular DCN as a target of a security threat.

The disclosure herein describes a virtual extensible local area network (VXLAN) gateway. During operation, the VXLAN gateway receives, from a physical host, an Ethernet packet destined for a virtual machine residing in a remote layer-2 network broadcast domain that is different from a local layer-2 network broadcast domain where the physical host resides. The VXLAN gateway then determines a VXLAN identifier for the received Ethernet packet. The VXLAN gateway further encapsulates the Ethernet packet with the virtual extensible local area network identifier and an Internet Protocol (IP) header, and forwards the encapsulated packet to an IP network, thereby allowing the packet to be transported to the virtual machine via the IP network and allowing the remote layer-2 network broadcast domain and the local layer-2 network broadcast domain to be part of a common layer-2 broadcast domain.

Method and system for virtual and physical network integration

Systems and methods are described for recommending security groups using graph-based learning models. A server can create a network graph that illustrates network flows between devices in a network and security groups that the devices belong to. The network graph can include nodes that represent the devices and security groups. The server can apply a graph-based learning model to learn embeddings of the nodes and create vectors using the embeddings. Using vectors of two nodes, the server can calculate a vector that represents an edge between the two nodes. The server can apply a binary classifier determine whether the edge should exist. A 'true' classification between two nodes can indicate that they should be able to communicate, and vice versa. A 'true' classification between a device node and a security group node can indicate that the device should be assigned to the security group, and vice versa.

Described herein are systems and methods to manage blacklists and duplicate addresses in software defined networks (SDNs). In one implementation, a method includes, in a control plane and data plane of an SDN environment, obtaining a blacklist for a logical port in the SDN environment. The method further includes deleting realized address bindings in a realized address list for the logical port that match the one or more address bindings in the blacklist and preventing subsequent address bindings that match the one or more address bindings in the blacklist from being added to the realized address list.

Management of blacklists and duplicate addresses in software defined networks

Described herein are systems and methods to manage Internet Protocol (IP) address discovery in a software defined networking (SDN) environment. In one example, a manager may generate an IP address discovery configuration and pass the IP address discovery configuration to a controller. Once received, the controller may obtain a discovered list from a hypervisor of one or more IP addresses associated with one or more logical ports and update a realized list for the one or more logical ports based on the discovered list and the IP address discovery configuration.

Management of endpoint address discovery in a software defined networking environment

Example methods and systems for logical network health check. One example may comprise obtaining network configuration information and network realization information associated with a logical network; processing the network configuration information and the network realization information to determine the following: (a) network configuration health information specifying a network configuration issue and a first remediation action; and (b) network realization health information specifying a network realization issue and a second remediation action; and providing, to a user device, multiple user interfaces (UIs) specifying the first health information and the second health information along with a visualization of the logical network. In response to detecting an instruction initiated by the user device using at least one of the multiple UIs, the first remediation action or the second remediation action may be performed.

Logical network health check in software-defined networking (SDN) environments

Some embodiments provide a method for monitoring a distributed application. The method receives a request to perform data collection for the distributed application. The method identifies data compute nodes (DCNs) that implement the distributed application. The method sends commands to host machines on which the identified DCNs operate to detect events related to the DCNs and provide data regarding the detected events. The method uses the data regarding the detected events to generate a user interface (UI) display of the topology of the distributed application.

Growing community of inventors

San Jose, CA, United States of America

Margaret Angeline Petrus