System and methods are described for authenticating users across multiple environments within a cloud-computing environment. A system may receive an indication that a user authenticated within a first environment has performed an action specific to a second environment. The system determines whether a previous action was performed by the user specific to the second environment. In response to determining that no previous action was performed, the system retrieves a first token from an authentication database, wherein the first token is associated with authentication of the user within the first environment. The system then validates the first token, and then generates a second token associated with authentication of the user within the second environment.

A computer-implemented method for detecting replay attack comprises: obtaining at least one candidate transaction for adding to a blockchain, the obtained candidate transaction comprising a timestamp; verifying if the timestamp is within a validation range and if an identification of the candidate transaction exists in an identification database; and in response to determining that the timestamp is within the validation range and the identification does not exist in the identification database, determining that the candidate transaction is not associated with a replay attack.

Methods and systems for account authentication in a distributed computing node group may involve sending a message to a member, the message having a first timestamp, increasing an authentication failure count, receiving a first key-exchange message from the member, the first key-exchange message having a second timestamp, evaluating the second timestamp, and determining whether to ignore the first key-exchange message based on an evaluation of the second timestamp. The first timestamp may be associated with a message received from the member prior to sending the message with the first timestamp to the member. The first key-exchange message may include a value computed by the member based on a group passcode shared with the member. The evaluation of the second timestamp may be based on at least one of a default value, the authentication failure count, or a timestamp associated with the group passcode.

A content streaming service method for reducing communication cost and a system therefore are provided. The method includes receiving, by an electronic device, a file of content in a streaming scheme from a server through a network, and reproducing the content through an application driven in the electronic device, storing, by the electronic device, the received file in a local storage, generating, by the electronic device, a local server, the local server configured to operate in association with a memory of the electronic device, retrieving, by the electronic device, the file stored in the local storage according to the streaming scheme through the local server, and re-reproducing the content through the application.

Various embodiments of the present invention set forth techniques for security monitoring of a network connection, including analyzing network traffic data for a network connection associated with a computing device, identifying one or more network traffic metrics for the network connection based on the network traffic data, determining that the network connection corresponds to at least one network connection profile based on the one or more network traffic metrics, detecting a potential security threat for the network connection based on the one or more network traffic metrics and the at least one network connection profile, and initiating a mitigation action with respect to the network connection in response to detecting the potential security threat. Advantageously, the techniques allow detecting potential security threats based on network traffic metrics and categorizations, without requiring monitoring of the content or the total volume of all traffic exchanged via the connection.

A data processing system is disclosed for data processing, including database and file management, as well accessing one or more databases or other data structures, authenticating users, and categorizing data items for addition to the database system. In some embodiments, the system may be configured to coordinate access to user account information via user-provided authentication credentials; apply account identification rules to the accessed account information to identify a plurality of accounts of the user; and initiate updates to a database record associated with the user indicative of any accounts identified.

Techniques are described for processing anomalies detected using user-specified rules with anomalies detected using machine-learning based behavioral analysis models to identify threat indicators and security threats to a computer network. In an embodiment, anomalies are detected based on processing event data at a network security system that used rules-based anomaly detection. These rules-based detected anomalies are acquired by a network security system that uses machine-learning based anomaly detection. The rules-based detected anomalies are processed along with machine learning detected anomalies to detect threat indicators or security threats to the computer network. The threat indicators and security threats are output as alerts to the network security system that used rules-based anomaly detection.

A method for tokenizing credentials is disclosed. In addition to a token, a verification value can be provided for each interaction. The verification value can be generated based at least in part on a dynamic data element. The dynamic data element may be kept secret, while the verification value can be distributed for use during an interaction. When the verification value is used, it can be validated by re-creating the verification value based at least on the stored dynamic data element.

A method for tokenizing credentials is disclosed. In addition to a token, a verification value can be provided for each interaction. The verification value can be generated based at least in part on a dynamic data element. The dynamic data element may be kept secret, while the verification value can be distributed for use during an interaction. When the verification value is used, it can be validated by re-creating the verification value based at least on the stored dynamic data element.

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is big data driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.