A lifelong spam emails classification model
TL;DR: An enhanced model is proposed for ensuring lifelong spam classification model and the overall performance of the suggested model is contrasted against various other stream mining classification techniques to prove the success of the proposed model as a lifelong spam emails classification method.
About: This article is published in Applied Computing and Informatics.The article was published on 2020-01-23 and is currently open access. It has received 17 citations till now.
Citations
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TL;DR: In this article , the authors present the consequences of ignoring the problem of dataset shift in spam email detection and show that this shift may lead to severe degradation in the estimated generalization performance, with error rates reaching values up to $48.81.
Abstract: Abstract Spam emails have been traditionally seen as just annoying and unsolicited emails containing advertisements, but they increasingly include scams, malware or phishing. In order to ensure the security and integrity for the users, organisations and researchers aim to develop robust filters for spam email detection. Recently, most spam filters based on machine learning algorithms published in academic journals report very high performance, but users are still reporting a rising number of frauds and attacks via spam emails. Two main challenges can be found in this field: (a) it is a very dynamic environment prone to the dataset shift problem and (b) it suffers from the presence of an adversarial figure, i.e. the spammer. Unlike classical spam email reviews, this one is particularly focused on the problems that this constantly changing environment poses. Moreover, we analyse the different spammer strategies used for contaminating the emails, and we review the state-of-the-art techniques to develop filters based on machine learning. Finally, we empirically evaluate and present the consequences of ignoring the matter of dataset shift in this practical field. Experimental results show that this shift may lead to severe degradation in the estimated generalisation performance, with error rates reaching values up to $$48.81\%$$ 48.81 % .
15 citations
TL;DR: An innovative feature selection algorithm is proposed and is called “the Highest Wins” (HW), which is used for building a naive Bayes and decision tree intrusion detection classifiers using the well-known dataset from Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD).
Abstract: The rapid advancement of Internet stimulates building intelligent data mining systems for detecting intrusion attacks The performance of such systems might be negatively affected due to the big datasets employed in the learning phase Determining the appropriate group of features within training datasets is an essential phase when building data mining classification models Nevertheless, the resulted minimized set of features should maintain or even improve the performance of the classification models Throughout this article, an innovative feature selection algorithm is proposed and is called “the Highest Wins” (HW) To evaluate the generalization ability of HW, it has been applied for creating classification models using naive Bayes technique from 10 benchmark datasets The obtained results were compared against two well-known strategies, namely chi-square and information gain The experimental results confirmed the competitiveness ability of the suggested strategy in terms of various evaluation measurements such as recall, precision, and error rate while significantly decreasing the number of selected features in datasets Further, the HW is used for building a naive Bayes and decision tree intrusion detection classifiers using the well-known dataset from Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) The results were promising not just in terms of overall performance, but also in terms of the time needed to build the classification model
13 citations
TL;DR: An “Improved RI algorithm” (IRI) is offered which decreases the searching space for generating classification rules by removing all unimportant candidate rule-items along the way of creating the classification model.
7 citations
TL;DR: Point-Biserial correlation is applied to each feature concerning the class label of the University of California Irvine spambase email dataset to select the best features and evaluates the performance of applied classifiers.
6 citations
TL;DR: In this article, a hybrid technique is created by combining the Naive Bayes Algorithm and the Markov Random Field, which is used to determine the prevalence and configuration of values in a dataset and perform a basic probabilistic classification operation in this proposed hybrid technique.
6 citations
References
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TL;DR: Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.
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