An autonomous online malicious spam email detection system using extended RBF network

Ali, Siti Hajar Aminah; Ozawa, Seiichi; Nakazato, Junji; Ban, Tao; Shimamura, Jumpei

doi:10.1109/ijcnn.2015.7280826

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Various techniques have been introduced for identifying spams, including statistical techniques, expert systems, Bayesian networks, neural networks, fuzzy logic, and collective intelligence algorithms. A Radial Basis Function (RBF) model is set forward alongside with Support Vector Machines (SVM) technique for identifying spam emails in [9]. RBF, an artificial neural network model, is used for training and testing data; and SVM, a classification technique, is used for mapping the features.…”

Section: Related Workmentioning

confidence: 99%

Detection of Spam Email by Combining Harmony Search Algorithm and Decision Tree

Gashti

2017

Eng. Technol. Appl. Sci. Res.

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Spam emails is probable the main problem faced by most e-mail users. There are many features in spam email detection and some of these features have little effect on detection and cause skew detection and classification of spam email. Thus, Feature Selection (FS) is one of the key topics in spam email detection systems. With choosing the important and effective features in classification, its performance can be optimized. Selector features has the task of finding a subset of features to improve the accuracy of its predictions. In this paper, a hybrid of Harmony Search Algorithm (HSA) and decision tree is used for selecting the best features and classification. The obtained results on Spam-base dataset show that the rate of recognition accuracy in the proposed model is 95.25% which is high in comparison with models such as SVM, NB, J48 and MLP. Also, the accuracy of the proposed model on the datasets of Ling-spam and PU1 is high in comparison with models such as NB, SVM and LR.

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Section: Related Workmentioning

confidence: 99%

Detection of Spam Email by Combining Harmony Search Algorithm and Decision Tree

Gashti

2017

Eng. Technol. Appl. Sci. Res.

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“…Classification is also an application of supervised machine learning, where a certain procedure will enable a machine to learn B Walid Mohamed Aly walid.ali@aast.edu 1 College of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria, Egypt from a training data set with each record that was previously labeled, and this is known as the training phase. The output of the training phase will be a well-defined classifier that can be used to predict the class of a new record that was not exposed during training.…”

Section: Introductionmentioning

confidence: 99%

“…Applications of classification span a wide range; it has been applied to problems like spam e-mail detection [1], cancer classification [2] and image classification [3].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Classifier Model Selection and Tuning Using Logistic Regression and Genetic Algorithms

Aly

2016

Arab J Sci Eng

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Logistic regression is an efficient machine learning procedure, and it is applied to build a mathematical model for classifying a certain input to a certain class among a number of preset classes. One of the main limitations of the standard classification approaches is the sensitivity to model structure, and another limitation is the sensitivity to the chosen value of regularization parameter λ that affects the estimation of the generalization error of candidate model, as any wrong value might cause underfitting or overfitting. In this research, a new approach for building a classifier model based on logistic regression is proposed, and the new algorithm depends on genetic algorithm to choose the effective model structure and also utilizes a proposed procedure to tune the regularization parameter aiming to find better model parameters. A case study for prediction of a diabetic patient using Pima Indian data set is included, and results showed the high effectiveness of the proposed approach as it reached a simpler model with a higher accuracy.

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Research on sensitive content detection in social networks

Meng

2019

CCF Trans. Netw.

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An autonomous online malicious spam email detection system using extended RBF network

Cited by 3 publications

References 12 publications

Detection of Spam Email by Combining Harmony Search Algorithm and Decision Tree

Detection of Spam Email by Combining Harmony Search Algorithm and Decision Tree

A New Approach for Classifier Model Selection and Tuning Using Logistic Regression and Genetic Algorithms

Research on sensitive content detection in social networks

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