Abstract: A system includes a compute engine that implements artificial intelligence-based dynamic, predictive, statistical classification meta-algorithms for better information security threat identification. The closed loop output of this algorithm uses a dynamic weights model along with AI/ML/DL to identify diverse information security threats more comprehensively and more efficiently. This system evolves with time and self-corrects to adapt to the ever changing needs of cyber security.

Abstract: An example system can include a server that includes or is associated with a keyword recommendation module. The keyword recommendation module can be configured to select keywords for a search engine for use in a search engine marketing campaign, wherein the search engine provides more traffic to Internet content of a publisher than other search engines, for the keywords. The module can also be configured to determine, per keyword, user engagement with the Internet content resulting from the traffic provided by the search engine for the keywords, according to one or more of time spent viewing the Internet content, page views of the Internet content, and dwell times. The module can also be configured to score, per keyword, the keywords according to the determined user engagement with the Internet content, and generate keyword recommendations according to the scoring of the keywords.

Abstract: An system can include a server that includes or is associated with a keyword price recommendation module. The module can be configured to recommend an optimal price for a keyword, per referral event, in a search engine marketing campaign. For making such a recommendation, the module can be configured to determine revenue generated per referral event for the keyword according to a number of referral events for the keyword for a plurality of users and revenue generated for the keyword by the plurality of users. It can also be configured to select a return on investment percentage for the keyword, and determine the optimal price for the keyword according to the revenue generated per referral event and the return on investment percentage. Additionally or alternatively, the keyword price recommendation module can be configured to calculate the return on investment percentage for the keyword according to at least one market condition.

Abstract: An example system can include a server that includes or is associated with a keyword recommendation module. The keyword recommendation module can be configured to select keywords for a search engine for use in a search engine marketing campaign, wherein the search engine provides more traffic to Internet content of a publisher than other search engines, for the keywords. The module can also be configured to determine, per keyword, user engagement with the Internet content resulting from the traffic provided by the search engine for the keywords, according to one or more of time spent viewing the Internet content, page views of the Internet content, and dwell times. The module can also be configured to score, per keyword, the keywords according to the determined user engagement with the Internet content, and generate keyword recommendations according to the scoring of the keywords.

Abstract: An example system can include a server that includes or is associated with a keyword recommendation module. The keyword recommendation module can be configured to select keywords for a search engine for use in a search engine marketing campaign, wherein the search engine provides more traffic to Internet content of a publisher than other search engines, for the keywords. The module can also be configured to determine, per keyword, user engagement with the Internet content resulting from the traffic provided by the search engine for the keywords, according to one or more of time spent viewing the Internet content, page views of the Internet content, and dwell times. The module can also be configured to score, per keyword, the keywords according to the determined user engagement with the Internet content, and generate keyword recommendations according to the scoring of the keywords.

Abstract: A system includes a computer processor that is configured to implement an upper layer stateful application network journaling process. The process includes recording and managing individual client actions on each server in a network. The recording and managing of the individual client actions executes at an application layer of a network protocol stack of the network. The process also includes recording and managing a server status snapshot on a per client basis. The recording and managing of the server status snapshot executes at the application layer of the network protocol stack of the network.

Abstract: A system and method of ensuring usage-based compliance based on a predicted model topology are disclosed. The system includes an information topology that extracts information from at least one information object, a daemon that operates to monitor interactions with at least one information object, an analyzer that performs a statistical-based pattern analysis using the monitored interactions, and a compliance policy that receives input from the information topology. The compliance policy is adjusted based on the analyzer and any new rules, and compares the actual state to the desired state. The method includes monitoring a file system to capture user interaction with the file system, performing statistical-based pattern analysis on the captured interactions, adjusting a compliance policy based on the statistical analysis and any new rules, inputting an information topology into the compliance policy, and comparing the actual state to a desired state that is based on the adjusted compliance policy.

Abstract: An example system can include a server that includes or is associated with a keyword recommendation module. The keyword recommendation module can be configured to select keywords for a search engine for use in a search engine marketing campaign, wherein the search engine provides more traffic to Internet content of a publisher than other search engines, for the keywords. The module can also be configured to determine, per keyword, user engagement with the Internet content resulting from the traffic provided by the search engine for the keywords, according to one or more of time spent viewing the Internet content, page views of the Internet content, and dwell times. The module can also be configured to score, per keyword, the keywords according to the determined user engagement with the Internet content, and generate keyword recommendations according to the scoring of the keywords.

Abstract: An system can include a server that includes or is associated with a keyword price recommendation module. The module can be configured to recommend an optimal price for a keyword, per referral event, in a search engine marketing campaign. For making such a recommendation, the module can be configured to determine revenue generated per referral event for the keyword according to a number of referral events for the keyword for a plurality of users and revenue generated for the keyword by the plurality of users. It can also be configured to select a return on investment percentage for the keyword, and determine the optimal price for the keyword according to the revenue generated per referral event and the return on investment percentage. Additionally or alternatively, the keyword price recommendation module can be configured to calculate the return on investment percentage for the keyword according to at least one market condition.

Abstract: A system and method of ensuring usage-based compliance based on a predicted model topology are disclosed. The system includes an information topology that extracts information from at least one information object, a daemon that operates to monitor interactions with at least one information object, an analyzer that performs a statistical-based pattern analysis using the monitored interactions, and a compliance policy that receives input from the information topology. The compliance policy is adjusted based on the analyzer and any new rules, and compares the actual state to the desired state. The method includes monitoring a file system to capture user interaction with the file system, performing statistical-based pattern analysis on the captured interactions, adjusting a compliance policy based on the statistical analysis and any new rules, inputting an information topology into the compliance policy, and comparing the actual state to a desired state that is based on the adjusted compliance policy.

Abstract: A system includes a computer processor that is configured to implement an upper layer stateful application network journaling process. The process includes recording and managing individual client actions on each server in a network. The recording and managing of the individual client actions executes at an application layer of a network protocol stack of the network. The process also includes recording and managing a server status snapshot on a per client basis. The recording and managing of the server status snapshot executes at the application layer of the network protocol stack of the network.