Abstract: The behavior analysis engine can also detect malicious network addresses that are sent to networked devices in the local network. The network traffic hub identifies network communications that are transmitted through the local network that contain network addresses. The network traffic hub transmits (or sends) the network address to the behavior analysis engine and the behavior analysis engine extracts network address features from the network address. The behavior analysis engine then applies an execution model to the execution features to determine a confidence score for the network address that represents the execution model's certainty that the network address is malicious. The behavior analysis engine uses the confidence score to provide instructions to the network traffic hub as to whether to allow the networked device to receive the network address.

Abstract: The behavior analysis engine can identify malicious entities based on connections between the entity and other entities. The behavior analysis engine receives an entity from the network traffic hub and identifies entities that are connected to the entity within a threshold degree of separation. The behavior analysis engine applies a recursive process to the entity whereby the behavior analysis engine determines whether an entity is malicious based on whether its connections within a threshold degree of separation are malicious. The behavior analysis engine uses the maliciousness of the entities' connections to determine whether the entity is malicious and, if the entity is malicious, the behavior analysis engine may instruct the network traffic hub to block network communications associated with the malicious entity.

Abstract: A method and system for detecting malicious behavior from smart appliances within a network. Smart appliances have a certain level of intelligence that allows them to perform a specific role more effectively and conveniently. Network traffic data and appliance identification data is collected about smart appliances within a network. The data is sent to a behavior analysis engine, which computes confidence levels for anomalies within the network traffic that may be caused by malicious behavior. If the behavior analysis engine determines that malicious behavior is present in the network, it sends an instruction to a network traffic hub to block network traffic relating to the anomaly. In some embodiments, network traffic is blocked based on source-destination pairs. In some embodiments, network traffic is blocked from a device outside the network that is determined to be malicious.

Abstract: A method and system for detecting malicious behavior from smart appliances within a network. Smart appliances have a certain level of intelligence that allows them to perform a specific role more effectively and conveniently. Network traffic data and identification data is collected about smart appliances within a network. The data is sent to a behavior analysis engine, which computes confidence levels for anomalies within the network traffic that may be caused by malicious behavior. If the behavior analysis engine determines that malicious behavior is present in the network, it sends an instruction to a network traffic hub to block network traffic relating to the anomaly. In some embodiments, network traffic is blocked based on source-destination pairs. In some embodiments, network traffic is blocked from a device outside the network that is determined to be malicious.