Effectiveness and Limitations of Statistical Spam Filters

Spam is not only clogging the Internet traffic by consuming a hefty amount of network bandwidth but it is also a source for e-mail born viruses, spyware, adware and Trojan Horses. It is also used to carry out denial of service, directory harvesting and phishing attacks that directly cause financial losses. Further, the contents of spam are often offensive and contain adult oriented and fraudulent materials which are objectionable to recipients. Several anti-spam procedures are currently employed to distinguish spam from legitimate e-mails; however spammers and phishers employ dynamic spam structures to obfuscate email content to circumvent these procedures. Apart from other technological procedures various adaptive learning filters have been developed that have an ability to allow an algorithm to constantly learn what sort of e-mail’s or e-mail content a recipient would typically process and what to see in normal course of its business. These filters are based on complex statistical techniques that classify future e-mails based on the word content of accepted e-mails. The statistical techniques employed in these filters separate an incoming e-mail into tokens and assign a probability value to each token. The probability of each token are collectively used to calculate the overall spam probability and accordingly the incoming e-mail is scored as spam, probably spam or legitimate e-mail.