A Study of Content Based Spam Filtering Techniques

The Internet has almost become an indispensable part of everyday life. Internet usage will continue to grow and therefore online communication and information exchange is gaining immense popularity, affecting users? social and commercial lives. Along with the growth of e-mail and social networking websites, there has been an increased production of spam over the years. Spam is nothing but unsolicited messages. Many spam-filtering techniques based on supervised machine learning algorithms have been proposed to automatically classify email messages as spam or legitimate (ham). Naive Bayesian classifier is one of the most popular learning algorithms that give promising results in separating spam from legitimate mail. Artificial Immune System (AIS) is an area of research that bridges the disciplines of immunology, computer science and engineering. Many AIS models have been proposed to solve these problems. Some prevalent ones are Clonal selection and Negative selection algorithms. The main objective of this book is building an efficient content-based system for filtering email spam messages.