Abstract: Device scanning aspects are described. In certain aspects, the method includes performing a scan of a device based on a port forwarding policy.

Abstract: The disclosed technology teaches facilitate User and Entity Behavior Analytics (UEBA) by classifying a file being transferred as encrypted or not. The technology involves monitoring movement of a files by a user over a wide area network, detecting file encryption for the files using a trained classifier, wherein the detecting includes processing by the classifier some or all of the following features extracted from each of the files: a chi-square randomness test; an arithmetic mean test; a serial correlation coefficient test; a Monte Carlo-Pi test; and a Shannon entropy test, counting a number of the encrypted files moved by the user in a predetermined period, comparing a predetermined maximum number of encrypted files allowed in the predetermined period to the count of the encrypted files moved by the user and detecting that the user has moved more encrypted files than the predetermined maximum number, and generating an alert.

Abstract: An underneath large-capacity single-gas-pump foam generating device includes a liquid replenishment mechanism and a foam generating mechanism. The liquid replenishment mechanism includes a liquid replenishment groove and a liquid replenishment bottle. The liquid replenishment bottle has a bottle opening sleeved in the liquid replenishment groove. The foam generating mechanism includes a gas supply pump unit, a gas mixing groove cavity, a gas-liquid mixing structure, a gas inlet and a vent valve structure. A lower portion of the gas mixing groove cavity is communicated with a lower portion of the liquid replenishment groove, a liquid replenishment one-way valve is arranged between the gas mixing groove cavity and the liquid replenishment groove. The gas-liquid mixing structure is provided with a liquid outlet channel, a gas outlet channel and a mixing channel, an inlet end of the liquid outlet channel extends downwardly into a bottom portion of the gas mixing groove cavity.

Abstract: Systems, methods, and related technologies for self-training classification are described. In certain aspects, a plurality of device classification methods with associated models are accessed. Each of the classification methods have an associated reliability level. The models of classification methods with a higher reliability level than other classifications methods are used to train the models associated with lower reliability level. The trained models and associated classification methods are thus improved.

Abstract: Device scanning aspects are described. In certain aspects, the method includes configuring a port forwarding policy on a first device based on a network session information, performing a scan of a second device based on a port forwarding policy.

Abstract: Systems, methods, and related technologies for profiling an entity and classifying an entity based on a profile are described. In certain aspects, data associated with communications of a first entity on a network are accessed, behaviors are determined based on the data associated with the communications of the first entity, and sequences of the behaviors of the first entity are determined. A profile of the first entity is determined based on the sequences of the behaviors, the profile including a classification of the first entity, a state machine of the profile of the first entity is determined, the state machine being associated with the classification against which the behaviors can be matched, a second entity is detected coming onto the network, and responsive to detecting the second entity coming onto the network, the second entity is classified based on the state machine of the profile of the first entity.

Abstract: The technology disclosed relates to configuring IoT devices for policy enforcement. In particular, the technology disclosed relates to configuring a plurality of special-purpose devices on a network segment of a network to steer outbound network traffic to an inline secure forwarder on the network segment instead of a default gateway on the network segment. The inline secure forwarder is configured to route the outbound network traffic to a policy enforcement point for a policy enforcement.

Abstract: The technology disclosed relates to a method, system, and non-transitory computer-readable media that trains a cloud traffic classifier to classify cross-application communications as malicious command and control (C2) traffic or benign cloud traffic. The training uses blocks of malicious Hypertext Transfer Protocol (HTTP) transactions targeted at a plurality of cloud applications by a plurality of clients prequalified as malicious command and control (C2) cloud traffic, and also blocks of benign HTTP transactions targeted at the plurality of cloud applications by the plurality of clients prequalified as benign cloud traffic. A cloud traffic classifier is trained on the cross-application malicious training example set and on the cross-application benign training example set by processing the blocks of the malicious and benign HTTP transactions as inputs, and generating outputs that classify the training examples as respectively malicious C2 cloud traffic or benign cloud traffic.

Abstract: The technology disclosed relates to a method, system, and non-transitory computer-readable media that detects malicious communication between a command and control (C2) cloud resource on a cloud application and malware on an infected host, using a network security system. The network security system reroutes the cloud traffic to the network security system. The incoming requests of the cloud traffic are directed to a cloud application in the plurality of cloud applications, and wherein the cloud application has a plurality of resources. The network security system analyzes the incoming requests, determines that the incoming requests are targeted at one or more malicious resources in the plurality of resources.

Abstract: The disclosed technology teaches training a classifier that classifies a file being transferred as encrypted or not. The technology involves accessing a plurality of training sample files, each of which is accompanied by a label of encrypted or not encrypted, sampling a configurable number of bytes of each respective file, generating features from the sampled bytes, including generating at least three of the following features: a chi-square randomness test; an arithmetic mean test; a serial correlation coefficient test; a Monte Carlo-Pi test; a Shannon entropy test; applying the generated features to train coefficients of a classifier algorithm to classify the sample files as encrypted or not encrypted; and saving the trained coefficients and classifier, whereby the classifier is trained to classify the sample files as encrypted or not encrypted.

Abstract: The disclosed technology teaches facilitate User and Entity Behavior Analytics (UEBA) by classifying a file being transferred as encrypted or not. The technology involves monitoring movement of a files by a user over a wide area network, detecting file encryption for the files using a trained classifier, wherein the detecting includes processing by the classifier some or all of the following features extracted from each of the files: a chi-square randomness test; an arithmetic mean test; a serial correlation coefficient test; a Monte Carlo-Pi test; and a Shannon entropy test, counting a number of the encrypted files moved by the user in a predetermined period, comparing a predetermined maximum number of encrypted files allowed in the predetermined period to the count of the encrypted files moved by the user and detecting that the user has moved more encrypted files than the predetermined maximum number, and generating an alert.

Abstract: The technology disclosed relates to a DHCP server-based steering logic for policy enforcement on IoT devices. In particular, the technology disclosed provides a steering logic running on a DHCP server on a network segment of a network. The steering logic is configured to receive DHCP requests broadcasted to the DHCP server by a plurality of special-purpose devices on the network segment, access DHCP responses generated by the DHCP server for the DHCP requests, receive, from a device classification logic, a positive determination that special-purpose devices in the plurality of special-purpose devices are special-purpose devices and not general-purpose devices, modify the accessed DHCP responses by replacing the default gateway with an inline secure forwarder on the network segment, and send the modified DHCP responses to the special-purpose devices.

Abstract: The technology disclosed relates to a DHCP relay-based steering logic for policy enforcement on IoT devices. In particular, the technology disclosed provides a steering logic that is interposed between a plurality of special-purpose devices on a network segment of a network and a DHCP server on the network segment. The steering logic is configured to intercept DHCP requests broadcasted to the DHCP server by special-purpose devices in the plurality of special-purpose devices, forward the intercepted DHCP requests to the DHCP sever 522, receive, from the DHCP server, DHCP responses to the intercepted DHCP requests, receive, from a device classification logic, a positive determination that the special-purpose devices are special-purpose devices and not general-purpose devices, modify the received DHCP responses by replacing the default gateway with an inline secure forwarder on the network segment, and send the modified DHCP responses to the special-purpose devices.

Abstract: Systems, methods, and related technologies for profiling an entity and classifying an entity based on a profile are described. In certain aspects, accessing data associated with one or more communications of an entity is accessed and one or more behaviors based on the data associated with the one or more communications of the entity are determined. One or more sequences of the one or more behaviors of the entity are determined and a profile is determined based on the one or more sequences of the one or more behaviors, wherein the profile comprises a classification of the entity. The profile may then be stored.

Abstract: The technology disclosed relates to a steering logic for policy enforcement on IoT devices. In particular, the technology disclosed provides a system. The system comprises an in-network intermediary. The in-network intermediary is configured to receive outbound network traffic from a plurality of special-purpose devices on a network segment of a network. The outbound network traffic is directed at one or more out-of-network servers. The in-network intermediary is further configured to determine, from the outbound network traffic, metadata required for policy enforcement. The in-network intermediary is further configured to append the metadata to the outbound network traffic, and send the outbound network traffic appended with the metadata to a policy enforcement point for policy enforcement.

Abstract: The technology disclosed relates to a method, system, and non-transitory computer-readable media that classifies cloud traffic between a client and cloud application as malicious command and control (C2) cloud traffic or benign cloud traffic. A cloud traffic classifier, in communication with a network security system, is provided intercepted cloud traffic as an input, and generate an output that classifies the cloud traffic as malicious command and control (C2) cloud traffic or benign cloud traffic. The classifier may use signals such as beaconing behavior, anomalous entity, anomalous agent, anomalous username, anomalous username, anomalous agent, cat's paw behavior of the client, anomalous hostname access patterns, and/or malicious task sequence execution.

Abstract: The technology disclosed relates to a steering logic for policy enforcement on IoT devices. In particular, the technology disclosed provides a system. The system comprises an in-network intermediary. The in-network intermediary is configured to receive outbound network traffic from a plurality of special-purpose devices on a network segment of a network. The outbound network traffic is directed at one or more out-of-network servers. The in-network intermediary is further configured to determine, from the outbound network traffic, metadata required for policy enforcement. The in-network intermediary is further configured to append the metadata to the outbound network traffic, and send the outbound network traffic appended with the metadata to a policy enforcement point for policy enforcement.

Abstract: The technology disclosed relates to configuring IoT devices for policy enforcement. In particular, the technology disclosed relates to configuring a plurality of special-purpose devices on a network segment of a network to steer outbound network traffic to an inline secure forwarder on the network segment instead of a default gateway on the network segment. The inline secure forwarder is configured to route the outbound network traffic to a policy enforcement point for a policy enforcement.

Abstract: The technology disclosed relates to a DHCP server-based steering logic for policy enforcement on IoT devices. In particular, the technology disclosed provides a steering logic running on a DHCP server on a network segment of a network. The steering logic is configured to receive DHCP requests broadcasted to the DHCP server by a plurality of special-purpose devices on the network segment, access DHCP responses generated by the DHCP server for the DHCP requests, receive, from a device classification logic, a positive determination that special-purpose devices in the plurality of special-purpose devices are special-purpose devices and not general-purpose devices, modify the accessed DHCP responses by replacing the default gateway with an inline secure forwarder on the network segment, and send the modified DHCP responses to the special-purpose devices.

Abstract: The technology disclosed relates to a DHCP relay-based steering logic for policy enforcement on IoT devices. In particular, the technology disclosed provides a steering logic that is interposed between a plurality of special-purpose devices on a network segment of a network and a DHCP server on the network segment. The steering logic is configured to intercept DHCP requests broadcasted to the DHCP server by special-purpose devices in the plurality of special-purpose devices, forward the intercepted DHCP requests to the DHCP sever 522, receive, from the DHCP server, DHCP responses to the intercepted DHCP requests, receive, from a device classification logic, a positive determination that the special-purpose devices are special-purpose devices and not general-purpose devices, modify the received DHCP responses by replacing the default gateway with an inline secure forwarder on the network segment, and send the modified DHCP responses to the special-purpose devices.