eMail2Pop includes a built-in SPAM filtering function for your AOL® e-mail based upon the latest award winning open-source technology.

eMail2Pop Spam Filter Technology Features

• No learning necessary - Filter takes less than five minutes to configure
• Fully integrated into eMail2Pop - no extra downloads or programs
• Spam e-mail is marked as spam and encapsulated as an attachment to prevent execution of malicious scripts or identity tracking code
• Uses both expression pattern matching and Bayes self-teaching detection system
• Each spam e-mail comes with a short content preview and full scoring breakdown
• Spam e-mails are not automatically deleted - users can set e-mail client to sort spam e-mail in a Junk E-mail folder or delete the e-mail automatically
• Extensive customization features allows users to define sensitivity of filter
• Access to multiple online spam blacklist databases ensures maximum accuracy

Subscription Based Service

eMail2Pop's built-in spam filter requires a subscription.  Subscribers are entitled to spam filter engine updates and filter definition files (FDF) updates.  When a filter update is available, eMail2Pop can notify users of the update.

Subscriptions are available in 3 months, 6 months, 12 months, and unlimited time periods.  A subscription is not required if you do not wish to use eMail2Pop's integrated spam filtering features.  Subscriptions can be purchased and extended at any time at the license upgrade page.

Current pricing for an eMail2Pop spam filter subscription can be found on the order form.  A subscription for spam filtering is optional and will not affect any of eMail2Pop's regular functionality.

Scanning Functionality - How It Works

If eMail2Pop has a valid spam subscription, spam filtering is automatically activated.  When a POP3 e-mail client requests AOL e-mail to be downloaded, eMail2Pop will forward the e-mail to the spam filter for processing before forwarding the AOL mail message to the POP3 client.  The filtering activity is completely transparent and does not require any user intervention.

Message processing times vary depending upon computer processing speed and internet connection throughput.  Because the spam filter runs many analytical tests on patterns and expressions in e-mail messages, a 1Ghz CPU and 256MB of ram should be the bare minimum for a computer running eMail2Pop.  By default, the spam filter will also query multiple online databases for positive spam matches, so internet connection speed is also an important factor.  On average, each message takes 4-10 seconds to analyze on a broadband internet connection.

Bayesian Auto-learning Capability

Each e-mail message analyzed by eMail2Pop is broken down into individual words and phrases and loggimagesed into a database on the computer.  Over time, the Bayes system will adjust to identify new patterns in both spam and non-spam e-mail.  Words that are commonly shared in spam e-mails will be given a higher score, and words that are commonly associated with non-spam e-mails will give the message a lower score.  Because the Bayes system requires a sizable database of words before it can produce accurate results, Bayes scoring is not activated until at least 50 high-scoring spam and 50 low-scoring non-spam e-mails are entered into the database.

Analyzed Message Output - Results

eMail2Pop's spam filter utilizes a scoring system.  The filter analyzes e-mail messages with a list of expressions and rules.  When a match is made, points are added to the message.  In addition, points are added to the message when a positive match has been made with an online database.  The Bayes auto-learning database will also assign an applicable score based upon the likelihood of the e-mail being spam through an analysis of the word content.  If the overall score surpasses a threshold, it will be marked as spam.

If a message is marked as spam, eMail2Pop generates a new e-mail message with a content preview and a breakdown of scoring.  The suspected spam message is attached to this e-mail.  Suspected spam e-mails are sent to the POP3 e-mail client as an attachment to prevent the unwanted execution of malicious scripts and/or tracking devices commonly used by spammers to verify if an e-mail account is active.

Sample Analysis

AS_SEEN_ON, WRINKLES, LOSEBODYFAT - These are all common terms used in advertising for weight loss products.  Because terms such as as seen on TV are significantly more likely to appear in spam e-mails than non-spam, legitimate e-mails, they are assigned a score.  Note that Wrinkles is given an exceptionally high score for its specificity.

BAYES_99 - This subscore is from the comparison with past e-mails stored in the Bayes auto-learning database.  Previous e-mails that were identified as spam share many common characteristics with this e-mail, so a 99-100% probability of the e-mail being spam was assigned to this e-mail.  Due to the high certainty of this match, a high score was attributed by the Bayes filter.

SUBJ_ILLEGAL_CHARS - Spam e-mails usually have a jumble of letters in the subject to uniquely identify the e-mail.

SUSPICIOUS_RECIPS - This particular e-mail was sent to many similar e-mail addresses (such as abc123@aol.com, abc1234@aol.com, abc123@aol.com).  This is a standard system used by spammers.

RCVD_IN_DSBL / SORBS - These are online blacklist databases.  The spam filter verifies the sender's IP address against multiple online databases of known spammer IP addresses.  If a match is found, a score is assigned to the e-mail.

If the e-mail message is not spam, the filter score can still be accessed in the Headers section of the e-mail in most POP3 e-mail clients.

Blacklists and Whitelists

In addition to the spam filter's powerful, tiered scanning technology, e-mails from certain senders or entire domains can be designated as spam or non-spam.  eMail2Pop's tight integration with the spam filter allows users to set access to senders from recent e-mail messages with a few clicks of the mouse.