Aerohive Networks, Inc.

Palo Alto Networks, Inc.

Extreme Networks, Inc.

Zingbox, Ltd.

Jianlin Zeng

Changming Liu

Sreekanth Reddy

Yong Kang

Peter Wu

Liwu Liu

Xu Zou

Mei Wang

Mingliang Li

Peng Fan

Jun Du

Gong Cheng

Sultanbek Omurzakov

Dylan Stewart Spagnuolo

Aveek Kumar Das

A software bill of materials (SBOM) and vulnerability management system ('system') disclosed herein extracts software component-related identifiers for Internet of Things (IoT) devices using deep packet inspection. The system filters the identifiers by removing identifiers that match a blacklist of identifiers known to not correspond to software components. The system then populates SBOM fields using a database storing the filtered identifiers with a schema that is uniform across SBOM file formats and queries a vulnerability database with software components indicated in the filtered identifiers to identify vulnerabilities for each IoT device for security risk assessment.

Software bill of materials and vulnerability management via deep packet inspection

A system and method for performing automated learning of an Internet-of-Things (IoT) application are disclosed. The automated learning is based on generation of application-agnostic events, allowing the automated learning to be performed without prior knowledge of the IoT application.

IoT application learning

Management of IoT devices through a private cloud. An IoT device is coupled to a gateway. A request from the IoT device to connect to a private cloud, wherein the private cloud is used to manage IoT devices, is received at a private cloud control center agent. An identification of the IoT device is determined. The IoT device is onboarded, using the identification, for management through the private cloud. A device profile of the IoT device is generated. The flow of data to and from the IoT device is regulated through application of IoT rules according to the device profile of the IoT device.

Private cloud control

Wireless access points detect neighboring wireless access points in different subnets. Upon connecting with a wireless client, a wireless access point determines predictive roaming information for the wireless client. Predictive roaming information identifies the wireless client; its home network subnet; and includes connection information associated with the wireless client. The wireless access point forwards the predictive roaming information associated with a wireless client to neighboring wireless access points while the wireless client is still connected with the wireless access point. Neighboring wireless access points store received predictive roaming information. Upon connecting with a wireless client, a neighboring wireless access point determines if the wireless client matches the stored predictive roaming information. If so, the neighboring wireless access point uses the predictive roaming information to quickly connect with the wireless client and to establish a tunnel to redirect network traffic associated with the wireless client through to its home subnet.

Predictive and nomadic roaming of wireless clients across different network subnets

Airtime usage may be used as a factor in controlling network traffic flow to and from client devices via a wireless network interface. Received packets or other data are assigned to a quality of service profile. Additionally, a cost value for communicating the received data is determined at least in part based on an actual or estimated airtime usage for the received packet. The cost value is used to allocate wireless network airtime to data. The allocation of wireless network airtime may be varied dynamically based on operating conditions. The cost value may be based on factors including the airtime used to communicate data; whether the data is a retransmission; and wireless network overhead. The cost value of data may also be different depending on whether the data is being sent from a client device or to a client device.

Airtime-based packet scheduling for wireless networks

A network device of a subnet determines predictive roaming information for a wireless client. Predictive roaming information can identify the wireless client and a home network subnet of the wireless client. The network device provides predictive roaming information associated with a wireless client to neighboring subnets. Neighboring subnets store received predictive roaming information, and use the predictive roaming information if the wireless client roams to them.

Predictive roaming between subnets

Managing rogue devices in a network through a network backhaul. A rogue device is detected in a network and a rogue device message that includes the rogue device is sent to a plurality of switches in a backhaul of the network. The rogue device is added into a rogue monitor table. Whether the rogue device is In-Net or Out-Of-Net is determined using forwarding tables of the plurality of switches in the backhaul of the network and the rogue monitor table. Mitigation is performed using a nearest switch to the rogue device of the plurality of switches in the backhaul of the network if it is determined that the rogue device is In-Net.

Managing rogue devices through a network backhaul

A technique allows stations to utilize an equal share of resources (e.g., airtime or throughput). This prevents slow stations from consuming too many resources (e.g., using up too much air time). Fairness is ensured by selective dropping after a multicast packet is converted to unicast. This prevents slow stations from using more than their share of buffer resources. Multicast conversion aware back-pressure into the network layer can be used to prevent unnecessary dropping of packets after multicast to unicast (1:n) conversion by considering duplicated transmit buffers. This technique helps achieve airtime/resource fairness among stations.

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San Jose, CA, United States of America

Jianlin Zeng