Link farm detection using SVMLight tool

Saraswathi, D.; Kathiravan, A. Vijaya; Kavitha, R.

doi:10.1109/iccci.2012.6158833

Cited by 2 publications

(1 citation statement)

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“…The Web spam is not absent from this list, but as the request response paradigm of the HTTP protocol makes it impossible for spammers to actually "send" pages directly to the users, spammers try to deceive search engines and as a result break the trust that search engines establish with their users. All deceptive actions which try to increase the ranking of a page in search engines are generally referred to as Web spam or spamdexing (combination of "spam" and "index") [1]. In another definition of spam, the authors [3] describe spam as "Web spamming refers to those unethical actions of spammers who intended to mislead search engines and give some pages higher ranking than they deserve".…”

Section: A Taxonomy Of Web Spammentioning

confidence: 99%

Study on the Effectiveness of Spam Detection Technologies

Iqbal¹,

Abid²,

Ahmad³

et al. 2016

IJITCS

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Abstract-Nowadays, spam has become serious issue for computer security, because it becomes a main source for disseminating threats, including viruses, worms and phishing attacks. Currently, a large volume of received emails are spam. Different approaches to combating these unwanted messages, including challenge response model, whitelisting, blacklisting, email signatures and different machine learning methods, are in place to deal with this issue. These solutions are available for end users but due to dynamic nature of Web, there is no 100% secure systems around the world which can handle this problem. In most of the cases spam detectors use machine learning techniques to filter web traffic. This work focuses on systematically analyzing the strength and weakness of current technologies for spam detection and taxonomy of known approaches is introduced.

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Section: A Taxonomy Of Web Spammentioning

confidence: 99%