Abstract: Processes and machines are disclosed for detecting malicious email campaigns based on unique but similarly spelled attachments. Email log(s) from network appliances are retrieved, filtered, normalized, and converted into field-based organized data for comprehensive analysis. Cluster analysis is performed. Filenames of email attachments are transformed into numerical vectors and a cosine similarity termset analysis is performed on the numerical vectors. Data is organized into time bins for burst detection. Statistical analysis is performed on the time bins. Pattern recognition is performed to identify alphanumeric similarities in the filenames of the attached files in order to detect malicious email campaigns. Machine learning may be used to optimize the cosine similarity threshold and other query variables, and to update existing cybersecurity filters and firewalls. Mitigation can be performed to remove malicious emails that were delivered to recipient mailboxes.

Abstract: Processes and machines are disclosed for detecting malicious email campaigns based on unique but similarly spelled attachments. Email log(s) from network appliances are retrieved, filtered, normalized, and converted into field-based organized data for comprehensive analysis. Cluster analysis is performed. Filenames of email attachments are transformed into numerical vectors and a cosine similarity termset analysis is performed on the numerical vectors. Data is organized into time bins for burst detection. Statistical analysis is performed on the time bins. Pattern recognition is performed to identify alphanumeric similarities in the filenames of the attached files in order to detect malicious email campaigns. Machine learning may be used to optimize the cosine similarity threshold and other query variables, and to update existing cybersecurity filters and firewalls. Mitigation can be performed to remove malicious emails that were delivered to recipient mailboxes.

Abstract: Processes and machines are disclosed for detecting malicious email campaigns based on unique but similarly spelled attachments. Email log(s) from network appliances are retrieved, filtered, normalized, and converted into field-based organized data for comprehensive analysis. Cluster analysis is performed. Filenames of email attachments are transformed into numerical vectors and a cosine similarity termset analysis is performed on the numerical vectors. Data is organized into time bins for burst detection. Statistical analysis is performed on the time bins. Pattern recognition is performed to identify alphanumeric similarities in the filenames of the attached files in order to detect malicious email campaigns. Machine learning may be used to optimize the cosine similarity threshold and other query variables, and to update existing cybersecurity filters and firewalls. Mitigation can be performed to remove malicious emails that were delivered to recipient mailboxes.

Abstract: A method and system for targeting online advertisements. The method includes receiving a request for an advertisement from a publisher, wherein the request includes an identifying data uniquely associated with a user. The method includes sending a response to the publisher, wherein the response includes the advertisement and an image URL. The method includes receiving a request for the image identified by the image URL and a unique user cookie from the user. The method includes if the identifying data has been previously received, determining a unique user identifier associated with the user from the identifying data. The method includes if the identifying data has not been previously received, determining the unique user identifier associated with the user from the unique user cookie, and associating the identifying information with the unique user identifier.

Abstract: Exemplary systems and methods for developing a mobile network are provided. Exemplary methods include receiving a request to include a mobile network site in a ranked menu of mobile network sites, including the mobile network site in the ranked menu of mobile network sites, sending the ranked menu of mobile network sites to a mobile communications device, receiving a communication from the mobile communications device indicating that the mobile network site should have a different rank, processing the received communication to update the ranked menu of mobile network sites, and sending the updated ranked menu to the mobile communications device. Exemplary systems include a communications module that receives a request to include a mobile network site in a ranked menu of mobile network sites, sends the ranked menu of mobile network sites to a mobile communications device, and receives a communication from the mobile communications device indicating that the mobile network site should have a different rank.

Abstract: The present invention provides a system and method for optimized delivery of inventoried content to mobile networks. According to one embodiment, a remote inventoried content server is installed in the geographic locale of a publisher of content for users of mobile communications devices on a mobile network. The remote inventoried content server is remote from the residence of an inventoried content delivery service. The remote inventoried content server maintains and updates its inventoried content through periodic communication with the inventoried content delivery service, so that the remote inventoried content server can supply its local publishers with fresh inventoried content in the short times required but as yet unachievable for display through a mobile network. The remote inventoried content server may also incorporate some of the logic of the inventoried content delivery service, further increasing the efficiency of delivering inventoried content to a mobile network.