Yahoo! Inc.

Xiaopeng Xi

Vishwanath Tumkur Ramarao

Mark Risher

Anirban Kundu

Naveen Jamal

Lei Zheng

Bhasker Hariharan

Belle L Tseng

Anirban Dasgupta

Wei Chu

Dragomir Dimitrov Yankov

Jyh-Shin Shue

Jay Pujara

Martin Zinkevich

Stanley Ke Wei

Embodiments are directed towards generating a unified user account trustworthiness system through user account trustworthiness scores. A trusted group of user accounts may be identified for a given action by grouping a plurality of user accounts into tiers based on a trustworthiness score of each user account for the given action. The tiers and/or trustworthiness scores may be employed to classify an item, such as a message as spam or non-spam, based on input from the user accounts. The trustworthiness scores may also be employed to determine if a user account is a robot account or a human account. The trusted group for a given action may dynamically evolve over time by regrouping the user accounts based on modified trustworthiness scores. A trustworthiness score of an individual user account may be modified based on input received from the individual user account and input from other user accounts.

User trustworthiness

The invention provides for at least three processes for detecting the probability of abusive use of a message account for sending large amounts of unsolicited messages, such as spam, to other message accounts. For example, information provided at registration for a new message account can be processed to determine the likelihood of abusive use of that message account. Also, inbound messages can be processed to determine if the message account that sent the inbound message is abusing the use of that message account. Additionally, outbound messages can be processed to determine if the message account that is attempting to send an outbound message is abusing the use of that message account. Each of these three processes can operate separately or in any combination with each other to further improve the probability that abusive use of a message account will be detected promptly and accurately.

Detecting spam from a bulk registered e-mail account

Detection of outbound sending of spam

Detecting bulk fraudulent registration of email accounts

Multiple features of email traffic are analyzed and extracted. Feature vectors comprising the multiple features are created and cluster analysis is utilized to track spam generation even from dynamically changing or aliased IP addresses.

Classification and cluster analysis spam detection and reduction

Growing community of inventors

Sunnyvale, CA, United States of America

Xiaopeng Xi