Juniper Networks, Inc.

Harness Inc.

Jisheng Wang

Xiaoying Wu

Kaushik Adesh Agrawal

Patrick Timmons

Michael Baj

Ruchit Rajkumar Mehta

Kush Shah

Patrick John MeLampy

Randall Wayne Frei

Jie C Jiang

Prashant Kumar

Greg Schrock

Amin Toghi Eshghi

David Jea

Jing Cheng

Shmuel Shaffer

Robert J Friday

Jacob Thomas

Scott A McCulley

Wenfeng Wang

Abhiram Madhugiri Shamsundar

Gorakhanath Kathare

Gaurav Sunil Nandode

Yedunandan Siddalingappa-Vitalapur

Winson Benedict Fernandes

Yogesh B G

Charl Matthee

Swapnil Rajkumar Kura

Sanjay Nagaraj

Sujai Hajela

Min-Yi Shen

Sunalini Sankhavaram

Amit Pillay

Patrick John Melampy

Wesley Purvis

Jordan Batch

Pawan Gandhi

Huan Thien-Vu

Swapnil Kura

Yuan-Hsiang Lee

Sudheer Matta

A method for detecting volumetric attacks using multi-phase analysis of API calls is disclosed. The method includes collecting API calls data having API calls to and from protected actors. The method also includes identifying a triggering suspicious time window during which a first set of actors indicate suspicious volume spikes. Further, the method includes performing an API-sequence-based user behavior clustering to identify a subset of the API attack sequence and a second set of actors. Thereafter, the method includes correlating the activities of the second set of actors over a large period of time to identify the complete API attack sequence to extract an attack signature of the complete API attack sequence for detecting potential attacks.

Method and system for detecting volumetric attacks using multi-phase analysis of application programming interface calls

An example method includes receiving, by a software-defined networking in a wide area network (SD-WAN) system having a first WAN link and a second WAN link for an SD-WAN service, WAN link characterization data for the first WAN link over a time period; determining, by the SD-WAN system based on processing the WAN link characterization data for the first WAN link using a machine learning model trained with historical WAN link characterization data for one or more WAN links, an indicator of a predicted performance metric of the first WAN link at a future time; and reassigning, by the SD-WAN system based on the indicator, an application from the first WAN link to the second WAN link.

AI-assisted WAN link selection for SD-WAN services

Disclosed are methods for detecting misconfigured VLANs. In some embodiments, traffic on a VLAN across multiple access points is categorized. Traffic on the VLAN at a single access point is then also categorized. The categorization of the VLAN traffic at the single access point can be in response to, for example, communication errors or other conditions. The two categorizations are then compared to determine if the VLAN traffic at the AP is consistent with the VLAN traffic across a network (e.g., an enterprise network). If the VLAN traffic at the AP is generally consistent with that across the network, this may indicate that a downstream network component, such as a switch or router, is misconfigured. Thus, some embodiments programmatically reconfigure the downstream component to forward traffic for the VLAN.

Detecting VLAN misconfiguration

An example method includes receiving, from a network device, data indicating characterizations of network traffic on a plurality of ports of the network device; determining, by processing circuitry, for each port of the plurality of ports, an indicator of a port type for the port based on the data indicating the characterizations of network traffic on the plurality of ports, wherein the port type indicates a link type of network traffic exchanged by the port; and outputting, by the processing circuitry, the indicator of the port type to an output device.

Network port characterization

A network management system (NMS) configured to attribute a health issue of an access point (AP) to a classifier or sub-classifier and remedy the health affecting a user's experience. The NMS obtains network data associated with an AP device of a site. The NMS also determines whether the network data satisfies one or more criteria corresponding to a classifier of a service level expectation (SLE) metric for determining a health issue of the AP device. In response to determining the network data does not satisfy the criteria associated with the classifier, NMS attributes the health issue of the AP device to the classifier. In response to attributing the health issue of the AP device to the classifier, NMS performs a remedy action based on the classifier.

Access point health metrics

Techniques are described by which a network management system (NMS) is configured to provide identification of root cause failure through the detection of network scope failures. For example, the NMS comprises one or more processors; and a memory comprising instructions that when executed by the one or more processors cause the one or more processors to: generate a hierarchical attribution graph comprising attributes representing different network scopes at different hierarchical levels; receive network event data, wherein the network event data is indicative of operational behavior of the network, including one or more of successful events or one or more failure events associated with one or more client devices; and apply a machine learning model to the network event data and to a particular network scope in the hierarchical attribution graph to detect whether the particular network scope has failure.

Identifying root cause of failures through detection of network scope failures

Techniques are described for monitoring network performance and managing network faults in a computer network. A cloud-based network management system stores path data received from a plurality of network devices operating as network gateways for an enterprise network, the path data collected by each network device of the plurality of network devices for one or more logical paths of a physical interface from the network device over a wide area network (WAN). The network management system determines, based on the path data, one or more WAN link health assessments, wherein the one or more WAN link health assessments include a success or failure state associated with one or more of service provider reachability, physical interface operation, or logical path performance; and in response to determining the at least one failure state, outputs a notification including identification of a root cause of the at least one failure state.

Network performance monitoring and fault management based on wide area network link health assessments

Techniques are disclosed for a network management system (NMS) that performs quality of service (QoS) monitoring and troubleshooting of user experience issues occurring outside of a network managed by the NMS using data obtained from third-party sources. For example, an NMS obtains third-party data of a third-party application server or third-party service provider server from a third-party monitoring vendor. The NMS identifies a user experience issue indicated by the third-party data and stitches the third-party data to network data received from network devices. The NMS determines a root cause or a remedial action of the user experience issue based at least on the network data received from the one or more network devices. The NMS generates a notification for presentation to an administrator device which identifies the root cause or the remedial action of the user experience issue.

Third-party service and application data for quality of service

An example method includes receiving, by an SD-WAN system, WAN link characterization data for a plurality of WAN links of the SD-WAN system over a time period; and for each site of a plurality of sites of the SD-WAN system, generating, by the SD-WAN system, a local policy for the site, wherein generating the local policy is based on a machine learning model trained with the WAN link characterization data for the plurality of WAN links, and providing the local policy to an SD-WAN edge device of the site.

Adaptive location-based SD-WAN policies

Systems, devices and techniques for an adaptive application-specific probing scheme are disclosed. An example network device includes memory configured to store a network address and probe protocol usable for probing a first network device associated with a source of an application, and one or more processors configured to determine a network address and probe protocol usable for probing the first network device, wherein the first network device comprises a server that is responsive to the probing, the server executing the application for the data flow, or a closest network device, to the server, that is responsive to the probing. The one or more processors are also configured to send to a second network device at a location serviced by the application, a message specifying the network address and probe protocol usable for probing the first network device.

Adaptable software defined wide area network application-specific probing

Techniques are described for monitoring application performance in a computer network. For example, a network management system (NMS) includes a memory storing path data received from a plurality of network devices, the path data reported by each network device of the plurality of network devices for one or more logical paths of a physical interface from the given network device over a wide area network (WAN). Additionally, the NMS may include processing circuitry in communication with the memory and configured to: determine, based on the path data, one or more application health assessments for one or more applications, wherein the one or more application health assessments are associated with one or more application time periods for a site, and in response to determining at least one failure state, output a notification including identification of a root cause of the at least one failure state.

Predicting network issues based on historical data

Application service level expectation health and performance

An example system includes access point (AP) devices configured to provide a wireless network at a site; and a network management system that stores network data received from the AP devices, the network data collected by the AP devices or client devices associated with the wireless network, and one or more processors configured to: receive a time series of SLE metrics based on the network data, determine, based on the time series, whether a network event has occurred, in response to a determination that a network event has occurred, determine a root cause for the network event, and in response to a determination that the root cause of the network event is associated with an AP device, determine a classification of the AP device, and determine a network management action for the AP device based on the network event and the classification of the AP device.

Network management actions based on access point classification

Disclosed is a network management system that provides an interface to enable diagnostics and troubleshoot of a remotely managed multi-site network. Some embodiments provide a natural language interface, while other embodiments provide a chatbot type interface that communicates with a technician via traditional text information on a display screen. The diagnostic and troubleshooting capabilities search a central data store that receives device property information from each site of the multi-site network. Based on devices or users that match portions of the entity, queries to the data store are initiated to obtain additional data on the devices. A response to the query is then provided based on the properties of the devices.

Enhanced conversation interface for network management

This disclosure describes a network management system (NMS) configured to determine a particular network device of a plurality of network devices based on a first user input in a conversational assistant. The one or more processors are further configured to identify a set of actionable insights for the particular network device based on network data received from the plurality of network devices and determine a set of views of a dashboard based at least on the set of actionable insights, wherein each view of the set of views displays a portion of the network data received from the plurality of network devices. The one or more processors are further configured to select a view of the set of views of the dashboard based on a second user input in the conversational assistant and cause the dashboard to display the selected view.

Conversational assistant for obtaining network information

Growing community of inventors

Palo Alto, CA, United States of America

Jisheng Wang