Supervised classification of spam emails with natural language stylometry

Shams, Rushdi; Mercer, Robert E.

doi:10.1007/s00521-015-2069-7

Cited by 24 publications

(12 citation statements)

References 29 publications

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“…The combination of boosting and SVM outperformed single classifiers on several benchmark datasets in [70]. Similarly, boosting and bagging were reported to perform significantly better than NB and SVM in a stylometric spam filter [63]. Laorden et al [46] proposed an anomaly-based spam-filtering system that uses a data reduction algorithm on the labelled dataset, reducing processing time while maintaining high detection rates.…”

Section: Spam Filtering Using Machine Learning: a Literature Reviewmentioning

confidence: 99%

Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks

Barushka

Hájek

2018

Appl Intell

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Rapid growth in the volume of unsolicited and unwanted messages has inspired the development of many anti-spam methods. Supervised anti-spam filters using machine-learning methods have been particularly effective in categorizing spam and non-spam messages. These automatically integrate spam corpora pre-processing, appropriate word lists selection, and the calculation of word weights, usually in a bag-of-words fashion. To develop an accurate spam filter is challenging because spammers attempt to decrease the probability of spam detection by using legitimate words. Complex models are therefore needed to solve such a problem. However, existing spam filtering methods usually converge to a poor local minimum, cannot effectively handle high-dimensional data and suffer from overfitting issues. To overcome these problems, we propose a novel spam filter integrating an N-gram tf.idf feature selection, modified distribution-based balancing algorithm and a regularized deep multi-layer perceptron NN model with rectified linear units (DBB-RDNN-ReL). As demonstrated on four benchmark spam datasets (Enron, SpamAssassin, SMS spam collection and Social networking), the proposed approach enables capturing more complex features from high-dimensional data by additional layers of neurons. Another advantage of this approach is that no additional dimensionality reduction is necessary and spam dataset imbalance is addressed using a modified distribution-based algorithm. We compare the performance of the approach with that of state-of-the-art spam filters (Minimum Description Length, Factorial Design using SVM and NB, Incremental Learning C4.5, and Random Forest, Voting and Convolutional Neural Network) and several machine learning algorithms commonly used to classify text. We show that the proposed model outperforms these other methods in terms of classification accuracy, with fewer false negatives and false positives. Notably, the proposed spam filter classifies both major (legitimate) and minor (spam) classes well on personalized / non-personalized and balanced / imbalanced spam datasets. In addition, we show that the proposed model performs better than the results reported by previous studies in terms of accuracy. * significantly higher (p=0.05).

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Section: Spam Filtering Using Machine Learning: a Literature Reviewmentioning

confidence: 99%

Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks

Barushka

Hájek

2018

Appl Intell

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“…The average classification accuracy of the proposed method was 95.15%. Shams and Mercer [47 ] proposed a filter by using natural language attributes. The accuracy of the filter applied to Enron data sets could reach 97.746%.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Effective spam filter based on a hybrid method of header checking and content parsing

Chu¹,

Hsu²,

Sheu

et al. 2020

IET Networks

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In recent years, hazardous e-mails arose, such as the e-mails infected with 'viruses' or 'worms' spreading destructive programs and the 'Phishing Mails' defrauding e-mail accounts of the users. The number of spams continue to grow. With the related problems of spam coming to be more severe, the spam topics have become significant in various research domains. The common filtering methods include black/white list, rule learning, and those based on text classification, such as Naïve Bayes, support vector machine, and boosting trees, multi-agent and genetic algorithm. Among these, the methods based on text classification are most widely applied. Moreover, some efficient methods were proposed to consider only the e-mail's header section, based on which both operating efficiency and classification efficiency could be improved. By applying machine learning technique and decision tree data mining algorithm C4.5, this study aims to propose an efficient spam filtering method with the following features: (i) proposing a two-phase filtering mechanism to scan mainly e-mail's header and auxiliary content. (ii) Reducing the problem of false positive. The experimental results show that the authors' method has a considerably high accuracy rate of 98.76%. Compared with some other methods of using the same spam data sets or of deep learning-based, their method obviously has an excellent performance.

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“…Moreover, in the problem of natural language processing, it is also natural to leverage the problems of part-of-speech (POS) tagging and noun chuck prediction, since a word with a POS of a noun usually appears in a noun chunk (Collobert and Weston, 2008;Herath, Ikeda, Ishizaki, Anzai and Aiso, 1992;Jabbar, Iqbal, Akhunzada and Abbas, 2018;Lyon and Frank, 1997;Shams and Mercer, 2016). Multi-task learning aims to build a joint model for multiple tasks from the same input data.…”

Section: Backgroundsmentioning

confidence: 99%

Low-Rank Deep Convolutional Neural Network for Multitask Learning

Shang

Wang³

2019

Computational Intelligence and Neuroscience

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In this paper, we propose a novel multitask learning method based on the deep convolutional network. The proposed deep network has four convolutional layers, three max-pooling layers, and two parallel fully connected layers. To adjust the deep network to multitask learning problem, we propose to learn a low-rank deep network so that the relation among different tasks can be explored. We proposed to minimize the number of independent parameter rows of one fully connected layer to explore the relations among different tasks, which is measured by the nuclear norm of the parameter of one fully connected layer, and seek a low-rank parameter matrix. Meanwhile, we also propose to regularize another fully connected layer by sparsity penalty so that the useful features learned by the lower layers can be selected. The learning problem is solved by an iterative algorithm based on gradient descent and back-propagation algorithms. The proposed algorithm is evaluated over benchmark datasets of multiple face attribute prediction, multitask natural language processing, and joint economics index predictions. The evaluation results show the advantage of the low-rank deep CNN model over multitask problems.

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Supervised classification of spam emails with natural language stylometry

Cited by 24 publications

References 29 publications

Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks

Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks

Effective spam filter based on a hybrid method of header checking and content parsing

Low-Rank Deep Convolutional Neural Network for Multitask Learning

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