Abstract: Creating and using learning models to identify botnet traffic can include obtaining netflow data associated with a connecting device that is communicating with a carrier network. The netflow data can represent communications associated with the connecting device. Data features associated with the communications can be extracted. The data features can include statistical information associated with the communications. A learning model based on the data features extracted from the netflow data can be trained. A prediction using the learning model can be generated, and an action based on the prediction can be taken.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a first group of Internet Protocol (IP) addresses from a group of network devices, and determining a second group of IP addresses from the first group of IP addresses includes possible malicious IP addresses utilizing a machine learning application. Further embodiments can include obtaining a first group of attributes of malicious IP addresses from a first repository, and determining a third group of IP addresses from the second group of IP addresses includes possible malicious IP addresses based on the first group of attributes. Additional embodiments can include receiving user-generated input indicating a fourth group of IP addresses from the third group of IP addresses includes possible malicious IP addresses, and transmitting a notification to a group of communication devices indicating that the fourth group of IP address includes possible malicious IP addresses. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a first group of Internet Protocol (IP) addresses from a group of network devices, and determining a second group of IP addresses from the first group of IP addresses includes possible malicious IP addresses utilizing a machine learning application. Further embodiments can include obtaining a first group of attributes of malicious IP addresses from a first repository, and determining a third group of IP addresses from the second group of IP addresses includes possible malicious IP addresses based on the first group of attributes. Additional embodiments can include receiving user-generated input indicating a fourth group of IP addresses from the third group of IP addresses includes possible malicious IP addresses, and transmitting a notification to a group of communication devices indicating that the fourth group of IP address includes possible malicious IP addresses. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a first group of Internet Protocol (IP) addresses from a group of network devices, and determining a second group of IP addresses from the first group of IP addresses includes possible malicious IP addresses utilizing a machine learning application. Further embodiments can include obtaining a first group of attributes of malicious IP addresses from a first repository, and determining a third group of IP addresses from the second group of IP addresses includes possible malicious IP addresses based on the first group of attributes. Additional embodiments can include receiving user-generated input indicating a fourth group of IP addresses from the third group of IP addresses includes possible malicious IP addresses, and transmitting a notification to a group of communication devices indicating that the fourth group of IP address includes possible malicious IP addresses. Other embodiments are disclosed.

Abstract: Creating and using learning models to identify botnet traffic can include obtaining netflow data associated with a connecting device that is communicating with a carrier network. The netflow data can represent communications associated with the connecting device. Data features associated with the communications can be extracted. The data features can include statistical information associated with the communications. A learning model based on the data features extracted from the netflow data can be trained. A prediction using the learning model can be generated, and an action based on the prediction can be taken.

Abstract: A networked computer system is provided for collecting and displaying historical content comprising a plurality of digital objects associated with a historical period or event. The computer system is comprised of one or more networked servers for processing the historical content. The servers are configured to access a database of host historical content input by an operator and user historical content input by a user, and display the historical content. One or more software applications running on the servers facilitate collection, integration and display of historical content. The software applications provide a template accessible to a user via a computer in communication with the network. The template is configured to allow the user to input the user historical content and relate a portion of the host historical content to create a user website integrating the user historical content with a portion of the host historical content.

Abstract: A networked computer system is provided for collecting and displaying historical content comprising a plurality of digital objects associated with a historical period or event. The computer system is comprised of one or more networked servers for processing the historical content. The servers are configured to access a database of host historical content input by an operator and user historical content input by a user, and display the historical content. One or more software applications running on the servers facilitate collection, integration and display of historical content. The software applications provide a template accessible to a user via a computer in communication with the network. The template is configured to allow the user to input the user historical content and relate a portion of the host historical content to create a user website integrating the user historical content with a portion of the host historical content.