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SECURING SOCIAL NETWORK MESSAGES USING MACHINE INFECTION HISTORY

IP.com Disclosure Number: IPCOM000217057D
Publication Date: 2012-Apr-30
Document File: 4 page(s) / 42K

Publishing Venue

The IP.com Prior Art Database

Related People

Vijay Seshadri: AUTHOR

Abstract

A method is described to aggregate an infection history of a social network user to compute a trust score for messages posted by the user. Data is combined from click stream logs and other infection submissions using a unique machine identifier to produce a trust score for each social network message.

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SECURING SOCIAL NETWORK MESSAGES USING MACHINE INFECTION HISTORY

Vijay Seshadri

Symantec Corporation

Abstract

A method is described to aggregate an infection history of a social network user to compute a trust score for messages posted by the user. Data is combined from click stream logs and other infection submissions using a unique machine identifier to produce a trust score for each social network message.

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SECURING SOCIAL NETWORK MESSAGES USING MACHINE INFECTION HISTORY

Problem Statement

Conventional tracking technology may be used to track machine infection histories for many users. Social networks, such as Facebook and Twitter, are commonly used as infection vectors by attackers. Infected machines may be programmed to post links, often as shortened URLs, to friends with special keywords that might spread the infection quickly to many users. In some cases, as many of 88% of malicious shortened URLs were clicked at least once suggesting that this is an effective method for attackers. Some security solutions provide protection after a malicious URL has been clicked by the user. However, users are not proactively warned about suspected malicious activity and links.

Publication Description

A method is described herein to compute a trust score for each social network message so that a user can be proactively warned before clicking a malicious link on a social networking site. First, data is acquired.  Various data sources may be used to retrieve data about machine infection history.  For example, click stream logs and risk assessment tools may provide data back to a central location. In one embodiment...