Palo Alto Networks, Inc.

Cisco Technology, Inc.

Jialiang Zhang

Fan Zhang

Ke Tian

Shengming Xu

Mingzhe Lu

Hongqing Li

Arun Athrey Chandrasekaran

Diana Leung

Madhusudan V Gindi

Vamsidhar Valluri

IoT adaptive threat prevention is disclosed. Network traffic received at a security platform is monitored to detect a plurality of IoT device profiles based on the monitored network traffic. A set of signatures for the security platform is received based on the detected plurality of IoT device profiles.

IoT adaptive threat prevention

Identifying Internet of Things (IoT) devices with packet flow behavior including by using machine learning models is disclosed. Information associated with a network communication of an IoT device is received. A determination of whether the IoT device has previously been classified is made. In response to determining that the IoT device has not previously been classified, a determination is made that a probability match for the IoT device against a behavior signature exceeds a threshold. Based at least in part on the probability match, a classification of the IoT device is provided to a security appliance configured to apply a policy to the IoT device.

IoT device identification with packet flow behavior machine learning model

The present technology discloses systems, methods, and computer-readable media to establish at least one target for a network, the target including at least one of an ingress parameter or an egress parameter and a policy for network packets; receive at least one network packet on the network; search for at least one matching target from the at least one targets, the at least matching target comprising parameters that match the at least one network packet; apply a policy in the at least one matching target to the at least one network packet; and forward the at least one network packet in accordance with the policy.

Systems and methods for implementing universal targets in network traffic classification

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San Jose, CA, United States of America

Jialiang Zhang