Scam Sensor for Outlook 1.0

Office Outlook add-in protects you from e-mail phishing scams.

November 1, 2005
Shareware
Windows 2k/XP
918 KB
1,208
1
Not rated yet    (0 votes)
Phishing is a modern plague of the Internet. Just last year, phishing scams resulted in $500 million losses. According to recent research, seventy percent of Internet users have never heard of phishing or are not even sure that it refers to e-mail scams which try to trick users into divulging sensitive information.

So, what is phishing? Phishing is a type of fraud aimed at stealing your identity or sensitive (usually financial) information, such as service account credentials, credit card numbers, passwords, social security numbers, etc. It does this by convincing you to provide the data by forging the information about the real recipient of your data. You may receive an e-mail that looks like it's been sent by your bank, but instead your reply will go to the cheater and your credit card number may be stolen and used without your knowledge.

Unfortunately, it's not enough to set up anti-spam filters to be protected from phishing e-mails. Usually such e-mails mimic e-mails sent from legit services and they pass anti-spam systems with ease. We offer you Scam Sensor: a solution that will dramatically increase the safety of e-mail communications for you, your family and your business.

Scam Sensor is an Outlook add-in that constantly monitors every e-mail the user reads. When Scam Sensor detects phishing techniques being used in the e-mail, it warns the user about the danger of visiting sites mentioned in this e-mail or replying to the message. The user does not have to click on any buttons to check a suspicious e-mail. Scam Sensor checks every e-mail the user reads automatically. The user cannot even turn Scam Sensor off and avoid the e-mail check. Scam Sensor does not use any online databases of fraud e-mails or forged hyperlinks. Instead, the program uses a set of special algorithms which use information received from an e-mail message to determine whether the message tries to cheat the reader. No algorithmic training is needed.