Artificial Immune System Based Classification Approach for Detecting Phishing Mails

Phishing/Spam is an attack that deals with social engineering methodology to illegally acquire and use someone else’s data on behalf of legitimate website for own benefits. Phishing emails are messages designed to fool the recipient into handing over personal information, such as login names, passwords, credit card numbers, account credentials, social security numbers etc. Fraudulent emails harm their victims through loss of funds and identity theft. They also hurt Internet business, because people lose their trust in Internet transactions for fear that they will become victims of fraud. Filtering approaches using blacklists are not completely effective as about every minute a new phishing scam is created. It has been investigated that the statistical filtering of phishing emails, where a classifier is trained on characteristic features of existing emails and subsequently is able to identify new phishing emails with different contents. This paper deals with the phishing detection problem and how to auto detect phishing emails. The proposed phishing detection model is based on the extracted email features to detect phishing emails, these features appeared in the header and HTML body of email. The developed model introduces Artificial Immune System methodology to classify whether the tested email is phishing or not.