Proposed efficient algorithm to filter spam using machine learning techniques

Abstract

Electronic spam is the most troublesome Internet phenomenon challenging large global companies, including AOL, Google, Yahoo and Microsoft. Spam causes various problems that may, in turn, cause economic losses. Spam causes traffic problems and bottlenecks that limit memory space, computing power and speed. Spam causes users to spend time removing it. Various methods have been developed to filter spam, including black list/white list, Bayesian classification algorithms, keyword matching, header information processing, investigation of spam-sending factors and investigation of received mails. This study describes three machine-learning algorithms to filter spam from valid emails with low error rates and high efficiency using a multilayer perceptron model. Several widely used techniques include C4.5 decision tree classifier, multilayer perceptron and Naïve Bayes classifier, all of which are used for training data whether in the form of spam or valid emails. Finally, the results are discussed, and outputs of considered techniques are examined in relation to the proposed model.