Abstract: The disclosure is directed to binding agents, e.g., a single chain variable fragment (scFv) that specifically binds to a flavivirus NS1 protein, as well as compositions comprising the binding agent and a method of using such compositions to induce an immune response against a flavivirus (e.g., a dengue virus). The disclosure also provides a conjugate comprising the binding agent, and a recombinant NS1 antigen.

Abstract: A system can efficiently control access to an interactive computing environment. The system can receive authentication data of an authentication attempt associated with an entity. The system can determine, for the entity, a historical vector including features that include sub-features. The historical vector can be determined by generating synthetic data, generating weights, and determining probabilities. The synthetic data can be based on historical authentication attempts by entities other than the entity. The weights can correspond to sub-features of the historical vector. The probabilities can indicate a likelihood that a corresponding sub-feature is involved in the authentication attempt. The system can compare the historical vector to the authentication data. The system can generate a responsive message based on the comparison for controlling access to the interactive computing environment.

Abstract: The present invention generally relates to cyclodextrin derivatives, including Dexolve®, sulfobutylether beta cyclodextrin interfering with the activity of the viral nonstructural protein NS1 produced by a flavivirus. More particularly, the present invention relates to method of treatment using cyclodextrin compounds for inhibiting or treating a condition resulting from a flavivirus infection, particularly vascular leakage condition and increased virus dissemination.

Abstract: A network traffic hub extracts encryption metadata from messages establishing an encrypted connection between a smart appliance and a remote server and determines whether malicious behavior is present in the messages. For example, the network traffic hub can extract an encryption cipher suite, identified encryption algorithms, or a public certificate. The network traffic hub detects malicious behavior or security threats based on the encryption metadata. These security threats may include a man-in-the-middle attacker or a Padding Oracle On Downgraded Legacy Encryption attack. Upon detecting malicious behavior or security threats, the network traffic hub blocks the encrypted traffic or notifies a user.

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.

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.

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.

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 network traffic hub extracts encryption metadata from messages establishing an encrypted connection between a smart appliance and a remote server and determines whether malicious behavior is present in the messages. For example, the network traffic hub can extract an encryption cipher suite, identified encryption algorithms, or a public certificate. The network traffic hub detects malicious behavior or security threats based on the encryption metadata. These security threats may include a man-in-the-middle attacker or a Padding Oracle On Downgraded Legacy Encryption attack. Upon detecting malicious behavior or security threats, the network traffic hub blocks the encrypted traffic or notifies a user.

Abstract: A network traffic hub extracts encryption metadata from messages establishing an encrypted connection between a smart appliance and a remote server and determines whether malicious behavior is present in the messages. For example, the network traffic hub can extract an encryption cipher suite, identified encryption algorithms, or a public certificate. The network traffic hub detects malicious behavior or security threats based on the encryption metadata. These security threats may include a man-in-the-middle attacker or a Padding Oracle On Downgraded Legacy Encryption attack. Upon detecting malicious behavior or security threats, the network traffic hub blocks the encrypted traffic or notifies a user.

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.

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.

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 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 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 network traffic hub extracts encryption metadata from messages establishing an encrypted connection between a smart appliance and a remote server and determines whether malicious behavior is present in the messages. For example, the network traffic hub can extract an encryption cipher suite, identified encryption algorithms, or a public certificate. The network traffic hub detects malicious behavior or security threats based on the encryption metadata. These security threats may include a man-in-the-middle attacker or a Padding Oracle On Downgraded Legacy Encryption attack. Upon detecting malicious behavior or security threats, the network traffic hub blocks the encrypted traffic or notifies a user.

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.

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 providing highly targeted advertising to remotely located viewers. The viewers are matched to an identifiable receiver on the system and, based on user preferences for products and services developed and stored on each user, targeted advertising for products and services each said user is likely to want, is substituted for the broadcaster advertising and communicated to the individual receivers matched with the user.

Abstract: A main rotor blade incorporating a rotor blade tip region having a tip chord and a tip taper such that the ratio of the main rotor blade chord to the tip chord is approximately 0.30. The tip taper ratio preferably providing an approximately 28% area of reduction in said outer 10% of said blade span. The tip region preferably further including a hover optimized high lift airfoil and a tip sweep that reaches a constant value of approximately 30 degrees along the quarter chord line.