In the cat-and-mouse game of the antispam industry, staying one step ahead of spammers is difficult because they constantly exploit the weaknesses of e-mail keyword filtering. But the newest artificial-intelligence filtering technology may adapt faster than the spammers can alter their messages.

Artificial intelligence techniques closely resemble the way our brains learn. Once we learn a skill, we use it to reason with. Using artificial intelligence to detect spam is done in the same way.

Natural-language processors serve as powerful artificial intelligence tools in the fight against spam. These processors, which actually are an array of complex algorithms, scan e-mail messages to discover the content of the messages. The algorithms are packaged into mail-filtering software, which generally sits outside a firewall or at an application service provider's network.

Artificial intelligence mail-filtering software accepts all in-bound e-mail traffic, routing legitimate traffic to a corporate SMTP server and flagging other messages as spam. Suspect e-mail is sent to a quarantine area where an administrator can view the contents to determine whether to discard it or pass it along.

Humans can quickly skim a message to judge if it is spam. Referencing keywords by their location in a sentence lets us understand the difference between "chicken breasts" as food and "bare breasts" as pornography. Similarly, natural-language algorithms break down messages into sentences and analyze their meaning.

With considerable processing effort, natural-language processing technology pieces together the meaning of messages by analyzing the words, sentences and paragraphs in the reverse order from which the algorithms originally took them apart.

Consider this e-mail example: "These delicious chicken breasts look good enough to eat - let's cook out tonight. If you can bring Bill, call me at work, 800-262-2222 x231. Oh, and check out the pictures from our last cookout at www.ophoto.com/2623/party_pictures>." A standard keyword analysis would flag the terms: breasts, look good, toll free number, Web site URL. But artificial intelligence analysis would determine the message was an invitation to dinner.

In the example, keyword-filtering technology picks out pieces of the sentence without really understanding its meaning. Its selective hearing incorrectly determines that the sentence is pornographic.

Whitepapers

• Secure Wireless Printing Options
• Getting in Compliance With Government Data Regulations By Leveraging Online Security Technology
• How to Offer the Strongest SSL Encryption
• Maximizing Site Visitor Trust Using Extended Validation SSL
• The Latest Advancements in SSL Technology
• Selecting Effective Virtual Directories

View more SOFTWARE whitepapers

Webcasts

• Managing the End User Experience-A Real World Example of Success
• Lowering the Cost of Email Archives
• Google Webcast: Why archive email? Top challenges and best practices
• High Availability for SQL Server 2005 Using Array-Based Replication and Host-Based Mirroring Technologies
• Technical Overview of Exchange Server 2007 with HP Servers and Storage
• Bringing Order and Security to your Mobile Workforce: Corporate Mobility Policy and Device Management On-Demand

View more SOFTWARE webcasts

Special Reports

• WAN Optimization: The Ultimate No Brainer
• Keeping Spam at Bay
• Application Performance Management Executive Guide
• Executive Guide: Building a Service Oriented Architecture
• Executive Guide: Virtualization Reality Check
• IT Buyers Guide To: Collaboration

View more SOFTWARE special reports