An Anti-Spam Detection Model for Emails of Multi-Natural Language

Abstract

The spam is one of the illegal and negative practices that involves the use of email services to send unsolicited emails such as phishing for the purpose of scamming which influences the reliability of email. Investigations have been conducted from various perspectives in order to examine this spam problem and how it affects society. In this regard, many studies have been carried out with the aim of studying the effect of spam activity on finance, economy, marketing, business and management, while other studies have focused on studying the influence of spam on security and privacy. Consequently, the literature affords various anti-spam methods that blocks or filters spam emails. This paper investigates the existing anti-spam methods, highlights some current problems and carries out an improved anti-spam model. In this regard, a new agent-based of Multi-Natural Language Anti-Spam (MNLAS) model is proposed. The MNLAS model process in the spam filtering process of an email both visual information such as images and texts in English and Arabic languages. The Jade agent platform and Java environments are employed in the implementation of MNLAS model. The MNLAS model is tested on a 200 emails' dataset and the results show that it is able to detect and filter various kinds of spam emails with high accuracy.

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

“…The confusion matrix is used to campout the classification accuracy of the MLP1 model, as illustrated in Figure 5. The MLP1 model achieves the best accuracy of 95.62% using (1) [25][26][27][28][29][30][31] after 100 epochs.…”

Two-Dimensional Optical Character Recognition of Mouse Drawn in Turkish Capital Letters Using Multi-Layer Perceptron Classification

“…There are about 10 11 nerve cells in the human brain. Figure 1 shows the relationship between a simplified biological brain nerve cell and its components [11]. ANNs, in other words, are computer programs that mimic biological neural networks.Although there are many types of ANNs, the most commonly used neural network structure is known as backpropagation ANN.ANNs are used successfully in the processing of uncertain, noisy and incomplete information [12].The basic ANN structure is shown in Figure 2.…”

Ear recognition system using random forest and histograms of oriented gradients techniques

“…Moreover, machine learning (ML) algorithms can present tools and techniques for classifying and distinguishing between two or more classes [12]- [14]. Also, these algorithms have proved their efficiency and effectiveness in different domains such as voice pathology detection [15]- [18], vehicle detection [19], identification of spam emails [20], images classification in the medical domain [21], [22], detection of conflict flows in SDN [23], and language identification [24]- [26]. Furthermore, these algorithms have used efficiently and as a major part in the ear recognition systems [27]- [29].…”