Abstract: Techniques for detecting and/or handling target attacks in an enterprise's email channel are provided. The techniques include receiving aspects of an incoming email message addressed to a first email account holder, selecting a recipient interaction profile and/or a sender profile from a plurality of predetermined profiles stored in a memory based upon the received properties, determining a message trust rating associated with the incoming email message based upon the incoming email message and the selected recipient interaction profile and/or the sender profile; and generating an alert identifying the incoming email message as including a security risk based upon the determined message trust rating.

Abstract: Techniques for detecting and/or handling target attacks in an enterprise's email channel are provided. The techniques include receiving aspects of an incoming email message addressed to a first email account holder, selecting a recipient interaction profile and/or a sender profile from a plurality of predetermined profiles stored in a memory based upon the received properties, determining a message trust rating associated with the incoming email message based upon the incoming email message and the selected recipient interaction profile and/or the sender profile; and generating an alert identifying the incoming email message as including a security risk based upon the determined message trust rating.

Abstract: Techniques for detecting and/or handling target attacks in an enterprise's email channel are provided. The techniques include receiving aspects of an incoming email message addressed to a first email account holder, selecting a recipient interaction profile and/or a sender profile from a plurality of predetermined profiles stored in a memory based upon the received properties, determining a message trust rating associated with the incoming email message based upon the incoming email message and the selected recipient interaction profile and/or the sender profile; and generating an alert identifying the incoming email message as including a security risk based upon the determined message trust rating.

Abstract: This disclosure describes a system, method, and apparatus for determining the likelihood of whether a digital document contains potentially malicious content by a scoring module configured to provide a page score for the digital document representing the likelihood that the document contains potentially malicious content, the scoring module using at least one of a Word Expression. The Word Expression is an equation having at least one variable representing a number of occurrences of potentially malicious content in the digital document. The scoring module is capable of providing both a real-time and a post-production evaluation of the digital document, and contributes an output value representing the calculated likelihood of potentially malicious content being present in the digital document. The scoring module is also configured to utilize inheritance, such that the digital document score is based on formulas within its own report and also one or more of one or more parent reports.

Abstract: Methods and systems for detecting social engineering attacks comprise: extracting one or more non-semantic data items from an incoming email; determining whether the one or more non-semantic data items match information stored in a data store of previously collected information; performing behavioral analysis on the one or more non-semantic data items; analyzing semantic data associated with the email to determine whether the non-semantic data matches one or more patterns associated with malicious emails; and based on the determining, performing, and analyzing, identifying the email as potentially malicious or non-malicious. The system also includes processes for collecting relevant information for storage within the data store and processes for harvesting information from detected social engineering attacks for entry into the data store and seeding of the collection processes.

Abstract: A solid shaped composite adsorbent for reducing deactivated catalyst residues and contaminants from a post polyolefin solution polymerization mixture is disclosed. The composite adsorbent comprises 70-90 wt % of an alumina component; 30-10 wt % of a clay component; and 0.5-3.5 wt % of at least one alkali metal component selected from the group consisting of elements in Group 1A of the modern periodic table.

Abstract: A solid shaped composite adsorbent for reducing deactivated catalyst residues and contaminants from a post polyolefin solution polymerization mixture is disclosed. The composite adsorbent comprises 70-90 wt % of an alumina component; 30-10 wt % of a clay component; and 0.5-3.5 wt % of at least one alkali metal component selected from the group consisting of elements in Group 1A of the modern periodic table.

Abstract: Methods and systems for collecting and processing large volumes of data to determine the relevancy and value thereof comprise: deploying one or more data retrieval modules to interface with one or more data sources within the local network, wherein the one or more data retrieval modules comprise executable program code; executing the one or more data retrieval modules to perform operations comprising: accessing application data representing data stored or communicated through the one or more data sources; and forwarding the application data for analysis; and analyzing the forwarded application data to determine whether it is relevant to the topic, wherein the analyzing is performed by one or more devices within the local network.

Abstract: Methods and systems for detecting social engineering attacks comprise: extracting one or more non-semantic data items from an incoming email; determining whether the one or more non-semantic data items match information stored in a data store of previously collected information; performing behavioral analysis on the one or more non-semantic data items; analyzing semantic data associated with the email to determine whether the non-semantic data matches one or more patterns associated with malicious emails; and based on the determining, performing, and analyzing, identifying the email as potentially malicious or non-malicious. The system also includes processes for collecting relevant information for storage within the data store and processes for harvesting information from detected social engineering attacks for entry into the data store and seeding of the collection processes.