E-Mail Spam Filtering: A Review of Techniques and Trends

Abstract

We present a comprehensive review of the most effective content-based e-mail spam filtering techniques. We focus primarily on Machine Learning-based spam filters and their variants, and report on a broad review ranging from surveying the relevant ideas, efforts, effectiveness, and the current progress. The initial exposition of the background examines the basics of e-mail spam filtering, the evolving nature of spam, spammers playing cat-and-mouse with e-mail service providers (ESPs), and the Machine Learning front in fighting spam. We conclude by measuring the impact of Machine Learning-based filters and explore the promising offshoots of latest developments.

Spam Filtering in Social Networks Using Regularized Deep Neural Networks with Ensemble Learning

“…To demonstrate the effectiveness of the proposed social network spam filter, we compared its performance with several methods used in previous studies for spam filtering [4][5][6][7][8], namely the single DNN, CNN (convolutional neural network), Naïve Bayes, k-NN (k nearest neighbour), C4.5 decision tree, MLP (multilayer perceptron), SVM (support vector machine), AIRS (artificial immune recognition system), Adaboost M1 with decision stump as base learner, and random forest. The settings of these algorithms were as follows: single DNN (the same setting as for the DNN with ensemble learning); CNN (mini-batch gradient descent algorithm with patch size 5×5 and max pool size 2×2, the remaining parameters were the same as for the DNN); k-NN (k = 3); C4.5 (J48 implementation with the confidence factor of 0.25 and minimum instances per leaf = 2); MLP (backpropagation with {10, 20, 50, 100} units in the hidden layer (50 units worked best), learning rate = 0.1, momentum = 0.2, and iterations = 1000); SVM (sequential minimal optimization algorithm with C = {2 0 , 2 1 , … , 2 6 } (C = 2 2 worked best) and polynomial kernel function); AIRS (AIRS2 parallel algorithm with affinity threshold = 0.2, clonal rate = 10, hyper-mutation rate = 2, k = 3 and stimulation threshold = 0.9); Adaboost M1 with 10 iterations and decision stump as base learner; and 100 random trees were used in random forest.…”

“…Machine learning techniques are particularly known to be highly accurate in detecting spam messages. There is a number of existing machine learning algorithms applied to spam filtering, including neural networks [4], support vector machines (SVMs) [5], Naïve Bayes [6], random forest [7], etc.…”

Text Mining in Big Data Analytics

“…Bhowmick and Hazarika [128] presented an exhaustive review of some of the frequently used content-based email spam filtering methods. They mostly focused on ML algorithms for spam filtering.…”

Hybrid Spam Filtration Method using Machine Learning Techniques

“…al. reviewed content-based [9] spam filtering techniques based on machine learning methods and achieved tremendous success.…”