Content Based Spam E-mail Filtering

Liu, Pingchuan; Moh, Teng-Sheng

doi:10.1109/cts.2016.0052

Cited by 27 publications

(15 citation statements)

References 12 publications

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“…This site is extremely an imagine or changed site. When clients go to this site, they're approached to enter individual information to be sent to the phishing assailant [1,2]. Phishing is regularly used to become familiar with somebody's secret phrase or charge card data.…”

Section: A a Standard Means For Inter Communicationmentioning

confidence: 99%

“…The streamlined highlights are sent into weka tool help support vector machine to group the website as genuine or forged. Liu P et al [16] had attempted to locate a viable answer for sifting spam messages in their work. The prescribed methodology for the investigating of the utilized content is about the E-mail as a watchword just to execute multiplex word handling.…”

Section: Existing Workmentioning

confidence: 99%

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Detecting Forged E-Mail using Data Mining Techniques

Sahoo,

Rajitha

2019

IJEAT

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In the modern era of computers E-mails is becoming a very important mode of communication for industry, people, and organizations and for the society as a whole. Especially in corporate sectors and business organizations, Emails are widely used for business and personal communication. The feature of E-mails is that, it creates quick, reliable type of communication that's all free and simply accessible. In spite of so many alternative means of communication such as messages, social networks like WhatsApp, Twitter, and mobile applications, the uses of E-mails continuously growing exponentially. But due to its popularity there are continuously threats and attacks are carried out over E-mails for various gain. The foremost popular attack over the web is phishing mails. Phishers utilize E-mail services quite expeditiously in spite of different detection and hindrance techniques already in situ. Most of the present day phishing attacks use E-mail as the primary carrier. Phishers conceive to fraudulently acquire sensitive information, like usernames, passwords and master card details, by masquerading as a trustworthy entity in transmission. Even though there are a lot of existing techniques offered to notice phishing attacks, every one of them have their own limitations. This research aims to identify the phishing E-mails using classification techniques with a better accuracy. The technique proposed in this research work to classify forged E-mails from the Genuine E-mails and also examines the effectiveness of detection of common user’s phishing E-mails. It provides a great help to the common man by proper detection of phishing attacks and protecting their confidential data.

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Section: A a Standard Means For Inter Communicationmentioning

confidence: 99%

Section: Existing Workmentioning

confidence: 99%

Detecting Forged E-Mail using Data Mining Techniques

Sahoo,

Rajitha

2019

IJEAT

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“…Spam mails or unsolicited mails can be categorized into two major types-i.) Content or text-based spam mail [12][13] and ii.) Image based spam mails [5][6].…”

Section: Prelimineriesmentioning

confidence: 99%

A New Machine Learning based Approach for Text Spam Filtering Technique

Sen¹,

Das²,

Chakraborty³

2017

CAE

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Electronic mail (e-mail) has become an essential element in our daily activities in recent past. Volume of email traffic is increasing many a fold in last couple of decades. Out of all such e-mails around 80% are unwanted mails, called as unsolicited bulk email (UBE) or spam mails. With the drastic increase in the use of electronic mail, there has also been an escalation in the problem of dealing with spam mails. In spite of availability of many commercial text based spam filters, users still suffer from the problem of spam mail, which unnecessarily accumulated in their inbox. In this work, we have proposed a spam detection algorithm based on Machine Learning approach. We have used the concept of Cumulative Weighted Sum (CWS) seeking to achieve a greater rate of accuracy in detecting spam mails. Three different techniques are also proposed for calculating CWS value. Our method is able to detect most of the spam and provides an accurate and dynamic filtration for such mails. Experimental results of our technique with different benchmark datasets are quite significant and gives much improved performance than the available text spam filters.

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“…The rough set theory is a mathematical tool for modeling incomplete or imprecise information [5]. It has been used for both feature selection and knowledge discovery in a number of real world domains; and, the rough set theory uses feature selection for classification of mails.…”

Section: Rough Set Theorymentioning

confidence: 99%

“…Peter J. Denning's article [17] is one of the first papers to consider this serious problem. The first mathematical apparatus applied to spam filtering systems is the Bayes' algorithm, which was used first by Sahami et.al in 1996 and then by the other researchers [5][6][7][8].…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

Performance of Machine Learning Techniques for Email Spam Filtering

2018

IJRTER

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Abstract-Email spam, an Unsolicited Commercial e-mail (UCE), is one of the major problems for today's Internet users. E-mail is an effective tool for communication as it saves time and cost, considerably. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. There are several machine learning techniques such as Bayesian classification, Naive bayes, k-NN, SVMs, Rough sets etc., which help us with a technique to filter spam. In this paper, an email-filtering approach that is based on supervised classifier has been proposed.The model mentioned here has the advantage of combining supervised machine learning with Support Vector Machines (SVM).To implement SVM, we are using LIBSVM, a library from MATLAB tool and compared it with existing classifiers. The proposed solution for the undesired spam could achieve better results in the system function and is faster when compared to the other existing machine learning techniques.

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Content Based Spam E-mail Filtering

Cited by 27 publications

References 12 publications

Detecting Forged E-Mail using Data Mining Techniques

Detecting Forged E-Mail using Data Mining Techniques

A New Machine Learning based Approach for Text Spam Filtering Technique

Performance of Machine Learning Techniques for Email Spam Filtering

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