SpamED: A spam E‐mail detection approach based on phrase similarity

E‐mail messages are unquestionably one of the most popular communication media these days. Not only are they fast and reliable but also free in general. Unfortunately, a significant number of e‐mail messages received by e‐mail users on a daily basis are spam. This fact is annoying since spam messages translate into a waste of the user's time in reviewing and deleting them. In addition, spam messages consume resources such as storage, bandwidth, and computer‐processing time. Many attempts have been made in the past to eradicate spam; however, none has proven highly effective. In this article, we propose a spam e‐mail detection approach, called SpamED, which uses the similarity of phrases in messages to detect spam. Conducted experiments not only verify that SpamED using trigrams in e‐mail messages is capable of minimizing false positives and false negatives in spam detection but it also outperforms a number of existing e‐mail filtering approaches with a 96% accuracy rate.