Is Email Business Dying?: A Study on Evolution of Email Spam Over Fifteen Years

De, Wang; Irani, Danesh; Pu, Calton

doi:10.4108/cc.1.1.e3

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Researchers pointed out that the contents and operating mechanism of spam emails change over time so the techniques that work now, may render useless in near future due to the change in structure and content of these spam emails; this phenomena is called Concept Drift [97], [98]. Wang et al [99] conducted a statistical analysis of spam emails over a period of 15 years (1998 -2013) and demonstrated how spammers adopt changes in not only spam contents, but also in the delivery mechanism.…”

Section: B Machine Learning Based Systemsmentioning

confidence: 99%

A Comprehensive Survey for Intelligent Spam Email Detection

et al. 2019

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The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject-appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection. INDEX TERMS Machine learning, phishing attack, spear phishing, spam detection, spam email, spam filtering.

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Section: B Machine Learning Based Systemsmentioning

confidence: 99%

A Comprehensive Survey for Intelligent Spam Email Detection

et al. 2019

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“…Topic models provide a simple way to analyze large volumes of unlabeled text [28]. Using contextual clues, topic models can connect words with similar meanings and distinguish between uses of words with multiple meanings.…”

Section: A Classification With Topic Modelingmentioning

confidence: 99%

Understanding Library User Engagement Strategies through Large-Scale Twitter Analysis

Zou

Chen

Dey

2015

2015 IEEE First International Conference on Big Data Computing Service and Applications

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Public libraries are increasingly using social media to connect their users in an innovative way. Librarians make use of social media with the purpose of "being part of their communities", or promoting libraries' services and events. Social media has become a significant platform for libraries to create their own participatory services emphasizing engagement with users. However, there has been little empirical investigation into the success of social media use by libraries. In this paper, we study the role of a recently popular social media, Twitter, in engaging users with a focus on public libraries. We use topicmodeling techniques to classify the library user engagement strategies into four categories -literature exhibits, engaging topic, community building, and library showcasing. These four engagement strategies are re-examined with the tweets collected from ten public libraries over three months. The tweets topic distribution of every library is discussed in the paper. Finally, the impacts of every strategy on user engagement have been evaluated by users feedback on every tweet. Through the data mining of public libraries' tweets, we aim to explore how user engagement strategies are used by the libraries on Twitter and suggest the best practices for libraries on social media initiatives to engage their users effectively.

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Using new scheduling heuristics based on resource consumption information for increasing throughput on rule-based spam filtering systems

Ruano-Ordás

Fdez-Glez

Fdez-Riverola

et al. 2015

Softw. Pract. Exper.

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Summary The large increase of spam deliveries since the first half of 2013 entailed hard to solve troubles in spam filters. In order to adequately fight spam, the throughput of spam filtering platforms should be necessarily increased. In this context, and taking into consideration the widespread utilization of rule‐based filtering frameworks in the spam filtering domain, this work proposes three novel scheduling strategies for optimizing the time needed to classify new incoming e‐mails through an intelligent management of computational resources depending on the Central Processing Unit (CPU) usage and Input/Output (I/O) delays. In order to demonstrate the suitability of our approaches, we include in our experiments a comparative study in contrast to other successful heuristics previously published in the scientific literature. Results achieved demonstrated that one of our alternative heuristics allows time savings of up to 10% in message filtering, while keeping the same classification accuracy. Copyright © 2015 John Wiley & Sons, Ltd.

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Is Email Business Dying?: A Study on Evolution of Email Spam Over Fifteen Years

Cited by 7 publications

References 31 publications

A Comprehensive Survey for Intelligent Spam Email Detection

A Comprehensive Survey for Intelligent Spam Email Detection

Understanding Library User Engagement Strategies through Large-Scale Twitter Analysis

Using new scheduling heuristics based on resource consumption information for increasing throughput on rule-based spam filtering systems

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