Symantec Corporation

Dylan Morss

Slawomir Grzonkowski

David Cowings

Sanford Jensen

Lamine Aouad

Ken Schneider

Gurujiwan Khalsa

Alejandro Mosquera Lopez

Emmet Cassidy

Mark Bruno

Each node of a metric tree comprises a similarity hash of a member of a dataset of known message threats, calculated using a given similarity hashing algorithm. The nodes are organized into the tree, positioned such that the differences between the similarity hashes are represented as distances between the nodes. Messages are received and tested to determine whether they are malicious. When a message is received, a similarity hash of the message is calculated using the same similarity hashing algorithm that is used to calculate the hashes of the members of the dataset. The tree is searched for a hash of a known message threat that is within a threshold of distance to the hash of the received message. Searching the tree can take the form of traversal from the root node, to determine whether the tree contains a node within the similarity threshold.

Detecting electronic messaging threats by using metric trees and similarity hashes

A method and system for simulating end user responses to spam email messages are described. In one embodiment, the method includes receiving a probe email message indicative of spam and identifying a seeding component in the probe email message. The method further includes activating the seeding component utilizing a selected email address. The selected email address may then appear to the originator of the probe email message as an active email account.

Method and apparatus for simulating end user responses to spam email messages

A method and apparatus for creating predictive filters for messages. In one embodiment, filter information is coupled to a reputation database. One or more filters for a message feature are generated if a reputation of the message feature is associated with one or more portions of the filter information. In one embodiment, SPAM filters are generated. In yet another embodiment, one or more message features are tested using heuristics. One or more message features are blacklisted based on a determination of the heuristics. One or more additional message filters are generated if a reputation of the message feature is associated with a blacklisted feature.

Method and apparatus for creating predictive filters for messages

Systems and methods for filtering fraudulent email messages are described. In one embodiment, a method includes receiving an email message, determining whether the email message is indicative of fraud, and creating a fraud filter based on the email message if the email message is fraudulent.

Growing community of inventors

Pleasant Hill, CA, United States of America

Dylan Morss