Spam Detection Using Feature Selection and Parameters Optimization

Lee, Sang Min; Kim, Dong-Seong; Kim, Ji Ho; Park, Jong Sou

doi:10.1109/cisis.2010.116

Cited by 47 publications

(22 citation statements)

References 11 publications

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“…4208 of them are edible while the rest are considered poisonous. Spambase dataset (Lee et al, 2010) consists of 4601 emails that are classified as spam or not. The two categories include 1813 and 2788 emails respectively.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Sparse Proximal Support Vector Machines for feature selection in high dimensional datasets

Pappu

Panagopoulos

Xanthopoulos

et al. 2015

Expert Systems with Applications

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Section: Accepted Manuscriptmentioning

confidence: 99%

Sparse Proximal Support Vector Machines for feature selection in high dimensional datasets

Pappu

Panagopoulos

Xanthopoulos

et al. 2015

Expert Systems with Applications

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“…Application of methods of clustering analyses to the problem of filtering e-mails to legitimate and spam is considered in papers [15][16][17][18]. From 2009 year, beginning from Paulo Cortez's, et al article [19] one can meet the statement as a Symbiotic Data Mining which is a hybrid of Collaborative Filtering (CF) and ContentBased Filtering (CBF).…”

Section: Historical Review Of Spam Filtering Methodsmentioning

confidence: 99%

Survey on Spam Filtering Techniques

Nazirova¹

2011

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In the recent years spam became as a big problem of Internet and electronic communication. There developed a lot of techniques to fight them. In this paper the overview of existing e-mail spam filtering methods is given. The classification, evaluation, and comparison of traditional and learning-based methods are provided. Some personal anti-spam products are tested and compared. The statement for new approach in spam filtering technique is considered.

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“…Luckner, Gad, & Sobkowiak suggested that spam might contain "weird combinations" which mean the existence obfuscated words represented by a string of lower case letters with some upper-case letters or digits among elements of the string such as Credit4U, v1agra and StaffForFree [16]. Lee et al has applied parameters optimization and feature selection on 57 features used in the Spambase dataset [17]. Their study showed that using only 19 features it was possibly to classify spam email with a 95.0011 detection rate.…”

Section: Spam Featuresmentioning

confidence: 99%

Improving Knowledge Based Spam Detection Methods: The Effect of Malicious Related Features in Imbalance Data Distribution

Alqatawna¹,

Faris²,

Al-Zewairi³

et al. 2015

IJCNS

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Spam is no longer just commercial unsolicited email messages that waste our time, it consumes network traffic and mail servers' storage. Furthermore, spam has become a major component of several attack vectors including attacks such as phishing, cross-site scripting, cross-site request forgery and malware infection. Statistics show that the amount of spam containing malicious contents increased compared to the one advertising legitimate products and services. In this paper, the issue of spam detection is investigated with the aim to develop an efficient method to identify spam email based on the analysis of the content of email messages. We identify a set of features that have a considerable number of malicious related features. Our goal is to study the effect of these features in helping the classical classifiers in identifying spam emails. To make the problem more challenging, we developed spam classification models based on imbalanced data where spam emails form the rare class with only 16.5% of the total emails. Different metrics were utilized in the evaluation of the developed models. Results show noticeable improvement of spam classification models when trained by dataset that includes malicious related features.

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Spam Detection Using Feature Selection and Parameters Optimization

Cited by 47 publications

References 11 publications

Sparse Proximal Support Vector Machines for feature selection in high dimensional datasets

Sparse Proximal Support Vector Machines for feature selection in high dimensional datasets

Survey on Spam Filtering Techniques

Improving Knowledge Based Spam Detection Methods: The Effect of Malicious Related Features in Imbalance Data Distribution

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