Abstract: A method for detecting anomalous streaming network traffic data in real time includes: creating an anomaly detection model including a singular value matrix and a data pattern matrix from a matrix of historical network traffic data; storing the singular value matrix and the data pattern matrix of the anomaly detection model; receiving streaming network traffic data; performing a log transform on the streaming network traffic data; applying the anomaly detection model to a matrix of the streaming network traffic data in real time as the streaming network traffic data is received; detecting anomalous patterns in the streaming network traffic data based on patterns identified by the anomaly detection model; and associating the anomalous patterns in the streaming network traffic data with IP addresses.

Abstract: A method for detecting anomalous streaming network traffic data in real time includes: creating an anomaly detection model including a singular value matrix and a data pattern matrix from a matrix of historical network traffic data; storing the singular value matrix and the data pattern matrix of the anomaly detection model; receiving streaming network traffic data; performing a log transform on the streaming network traffic data; applying the anomaly detection model to a matrix of the streaming network traffic data in real time as the streaming network traffic data is received; detecting anomalous patterns in the streaming network traffic data based on patterns identified by the anomaly detection model; and associating the anomalous patterns in the streaming network traffic data with IP addresses.

Abstract: A method for detecting anomalous streaming network traffic data in real time includes: creating an anomaly detection model including a singular value matrix and a data pattern matrix from a matrix of historical network traffic data; storing the singular value matrix and the data pattern matrix of the anomaly detection model; receiving streaming network traffic data; performing a log transform on the streaming network traffic data; applying the anomaly detection model to a matrix of the streaming network traffic data in real time as the streaming network traffic data is received; detecting anomalous patterns in the streaming network traffic data based on patterns identified by the anomaly detection model; and associating the anomalous patterns in the streaming network traffic data with IP addresses.

Abstract: A method for detecting anomalous streaming network traffic data in real time includes: creating an anomaly detection model including a singular value matrix and a data pattern matrix from a matrix of historical network traffic data; storing the singular value matrix and the data pattern matrix of the anomaly detection model; receiving streaming network traffic data; performing a log transform on the streaming network traffic data; applying the anomaly detection model to a matrix of the streaming network traffic data in real time as the streaming network traffic data is received; detecting anomalous patterns in the streaming network traffic data based on patterns identified by the anomaly detection model; and associating the anomalous patterns in the streaming network traffic data with IP addresses.

Abstract: Aspects of the subject disclosure may include, for example, embodiments include receiving first user-generated input from a recruiter device. The recruiter device is associated with a manager of a group. Further embodiments include generating a first smart contract between an applicant and the manager according to the first user-generated input. The generating of the first smart contract comprises generating the first smart contract on a blockchain. Additional embodiments can include providing the applicant access to the group according to the first smart contract, receiving second user-generated input from the recruiter device, and generating a second smart contract between a record keeper and the manager according to the second user-generated input. The generating of the second smart contract comprises generating the second smart contract on the blockchain. Also, embodiments can include providing the record keeper access to the group according to the second smart contract. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, embodiments include receiving first user-generated input from a recruiter device. The recruiter device is associated with a manager of a group. Further embodiments include generating a first smart contract between an applicant and the manager according to the first user-generated input. The generating of the first smart contract comprises generating the first smart contract on a blockchain. Additional embodiments can include providing the applicant access to the group according to the first smart contract, receiving second user-generated input from the recruiter device, and generating a second smart contract between a record keeper and the manager according to the second user-generated input. The generating of the second smart contract comprises generating the second smart contract on the blockchain. Also, embodiments can include providing the record keeper access to the group according to the second smart contract. Other embodiments are disclosed.

Abstract: A method for detecting anomalous streaming network traffic data in real time includes: creating an anomaly detection model including a singular value matrix and a data pattern matrix from a matrix of historical network traffic data; storing the singular value matrix and the data pattern matrix of the anomaly detection model; receiving streaming network traffic data; performing a log transform on the streaming network traffic data; applying the anomaly detection model to a matrix of the streaming network traffic data in real time as the streaming network traffic data is received; detecting anomalous patterns in the streaming network traffic data based on patterns identified by the anomaly detection model; and associating the anomalous patterns in the streaming network traffic data with IP addresses.

Abstract: Aspects of the subject disclosure may include, for example, embodiments include receiving first user-generated input from a recruiter device. The recruiter device is associated with a manager of a group. Further embodiments include generating a first smart contract between an applicant and the manager according to the first user-generated input. The generating of the first smart contract comprises generating the first smart contract on a blockchain. Additional embodiments can include providing the applicant access to the group according to the first smart contract, receiving second user-generated input from the recruiter device, and generating a second smart contract between a record keeper and the manager according to the second user-generated input. The generating of the second smart contract comprises generating the second smart contract on the blockchain. Also, embodiments can include providing the record keeper access to the group according to the second smart contract. Other embodiments are disclosed.

Abstract: A method is disclosed for managing application software in a mobile communication device including a first operating environment requiring an authorization from a user of the mobile communication device for managing of application software on top of the first operating environment, and a second operating environment installed on top of the first operating environment, the second operating environment requiring an authorization from the second operating environment only for managing of application software on top of the second operating environment. In at least one embodiment, the method includes receiving an authorization from the second operating environment to manage application software on top of the second operating environment; and managing the application software on top of the second operating environment.