Study on the Effectiveness of Spam Detection Technologies

Iqbal, Muhammad; Abid, Malik Muneeb; Ahmad, M.O.; Khurshid, Faisal

doi:10.5815/ijitcs.2016.01.02

Cited by 15 publications

(7 citation statements)

References 24 publications

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“…There is scope of research in this field and some research works have been conducted on it. There are different categories of SMS spam filtering such as white listing and black listing, content-based, non-content based, collaborative approaches and challenge-response technique [4], [5], [12], [29]. The techniques are used in client side, server side or in both client and server side [4].…”

Section: Background and Related Workmentioning

confidence: 99%

A Systematic Literature Review on SMS Spam Detection Techniques

Lota¹,

Hossain²

2017

IJITCS

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Spam SMSes are unsolicited messages to users, which are disturbing and sometimes harmful. There are a lot of survey papers available on email spam detection techniques. But, SMS spam detection is comparatively a new area and systematic literature review on this area is insufficient. In this paper, we perform a systematic literature review on SMS spam detection techniques. For that purpose, we consider the available published research works from 2006 to 2016. We choose 17 papers for our study and reviewed their used techniques, approaches and algorithms, their advantages and disadvantages, evaluation measures, discussion on datasets and finally result comparison of the studies. Although, the SMS spam detection techniques are more challenging than email spam detection techniques because of the regional contents, use of abbreviated words, unfortunately none of the existing research addresses these challenges. There is a huge scope of future research in this area and this survey can act as a reference point for the future direction of research.

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Section: Background and Related Workmentioning

confidence: 99%

A Systematic Literature Review on SMS Spam Detection Techniques

Lota¹,

Hossain²

2017

IJITCS

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“…Similarly, prospective areas of application of big data technology are: (1) airline traffic control; (2) market ing analysis; (3) electronic encyclopedia summarizat ion system as highlighted by Hatipoglu and Omurca in 2016 in their paper A Turkish Wikipedia Text Su mmarizat ion System for Mobile Devices [14]; (4) agro-industrial engineering projects; (5) stock e xchange predict ions; (6) spam detection mechanisms as underlined by Iqbal, Abid, Ahmad, and Khurshid in 2016 in their paper Study on the Effectiveness of Spam Detection Technologies [15]; political campaigns; economic research; drug discovery; mo lecular analysis; weather forecast; route maps analysis; music creation; colonoscopy analysis; and many others.…”

Section: Health Informaticsmentioning

confidence: 99%

Toward Grasping the Dynamic Concept of Big Data

Alvarez-Dionisi¹

2016

IJITCS

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The idea of big data represents a growing challenge for co mpanies such as Google, Yahoo, Bing, Amazon, eBay, YouTube, Linked In, Facebook, Instagram, and Twitter. However, the challenge goes beyond private companies, government agencies, and many other organizations. It is actually an alarm clock that is ringing everywhere: newspapers, magazines, books, research papers, online, offline, it is all over the world and people are worried about it. Its economic impact and consequences are of unproportioned dimensions. This research outlines the fundamental literature required to understand the concept of big data. Additionally, the present work provides a conclusion and recommendations for further research on big data. This study is part of an ongoing research that addresses the link between economic growth and big data.

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“…The sender of spam mails has no prior relationship with the receivers but gathers the addresses from different sources such as phone books and filled forms. Spam messages are fast growing to be one of the most serious threats to users of E-mail messages because it is a major means of sending threats, including viruses, worms and phishing attacks [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Classification Algorithms for Email Spam Detection

Abdulhamid¹,

Shuaib²,

Osho³

et al. 2018

IJCNIS

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Abstract-The increase in the use of email in every day transactions for a lot of businesses or general communication due to its cost effectiveness and efficiency has made emails vulnerable to attacks including spamming. Spam emails also called junk emails are unsolicited messages that are almost identical and sent to multiple recipients randomly. In this study, a performance analysis is done on some classification algorithms including: Bayesian Logistic Regression, Hidden Naï ve Bayes, Radial Basis Function (RBF) Network, Voted Perceptron, Lazy Bayesian Rule, Logit Boost, Rotation Forest, NNge, Logistic Model Tree, REP Tree, Naï ve Bayes, Multilayer Perceptron, Random Tree and J48. The performance of the algorithms were measured in terms of Accuracy, Precision, Recall, FMeasure, Root Mean Squared Error, Receiver Operator Characteristics Area and Root Relative Squared Error using WEKA data mining tool. To have a balanced view on the classification algorithms' performance, no feature selection or performance boosting method was employed. The research showed that a number of classification algorithms exist that if properly explored through feature selection means will yield more accurate results for email classification. Rotation Forest is found to be the classifier that gives the best accuracy of 94.2%. Though none of the algorithms did not achieve 100% accuracy in sorting spam emails, Rotation Forest has shown a near degree to achieving most accurate result.

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Study on the Effectiveness of Spam Detection Technologies

Cited by 15 publications

References 24 publications

A Systematic Literature Review on SMS Spam Detection Techniques

A Systematic Literature Review on SMS Spam Detection Techniques

Toward Grasping the Dynamic Concept of Big Data

Comparative Analysis of Classification Algorithms for Email Spam Detection

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