نتایج جستجو برای: email spam detection

تعداد نتایج: 656459  

2006
Yu-Ru Lin Wen-Yen Chen Xiaolin Shi Richard Sia Xiaodan Song Yun Chi Koji Hino Hari Sundaram Jun'ichi Tatemura Belle L. Tseng

Spam blogs (splogs) have become a major problem in the increasingly popular blogosphere. Splogs are detrimental in that they corrupt the quality of information retrieved and they waste tremendous network and storage resources. We study several research issues in splog detection. First, in comparison to web spam and email spam, we identify some unique characteristics of splog. Second, we propose...

2003
Jean-Marc Seigneur Anselm Lambert Patroklos G. Argyroudis Christian D. Jensen

Although there are different tools and technologies available to prevent attacks on privacy when online applications are used, few tools are available for detection of actions that violate privacy agreements. The loss of privacy when third parties obtain email addresses of users without their consent can be followed by unsolicited emails – known as spam – sent on the open communication channel....

2007
Slavisa Sarafijanovic Luis Hernández Raphael Naefen Jean-Yves Le Boudec

The existing tools for testing spam filters evaluate a filter instance by simply feeding it with a stream of emails, possibly also providing a feedback to the filter about the correctness of the detection. In such a scenario the evaluated filter is disconnected from the network of email servers, filters, and users, which makes the approach inappropriate for testing many of the filters that expl...

2014
Abu Awal Md Shoeb Dibya Mukhopadhyay Shahid Al Noor Alan Sprague Gary Warner

A substantial majority of the email sent everyday is spam. Spam emails cause many problems if someone acts or clicks on the link provided in the email body. The problems may include infecting users personal machine with malware, stealing personal information, capturing credit card information, etc. Since spam emails are generated as a part of a very limited numbers of spam campaigns, it is usef...

2006
Flavio D. Garcia Jaap-Henk Hoepman Jeroen van Nieuwenhuizen

Unsolicited bulk email (aka. spam) is a major problem on the Internet. To counter spam, several techniques, ranging from spam filters to mail protocol extensions like hashcash, have been proposed. In this paper we investigate the effectiveness of several spam filtering techniques and technologies. Our analysis was performed by simulating email traffic under different conditions. We show that ge...

2004
Flavio D. Garcia Jaap-Henk Hoepman Jeroen van Nieuwenhuizen

Unsolicited bulk email (aka. spam) is a major problem on the Internet. To counter spam, several techniques, ranging from spam filters to mail protocol extensions like hashcash, have been proposed. In this paper we investigate the effectiveness of several spam filtering techniques and technologies. Our analysis was performed by simulating email traffic under different conditions. We show that ge...

Journal: :International Journal of Instructional Technology and Educational Studies (Print) 2021

The email has become one of the most efficient and cost-effective methods communication in recent years. However, as number users grows, so does spam emails. Email management a big rising concern for both people companies consequence its sensitivity to abuse. Spam, or unsolicited sending unwanted messages, is example misuse. Spam defined bulk email, sent large without their consent. Half receiv...

2012
Jatinderkumar R. Saini

In recent times, the problem of Unsolicited Bulk Email (UBE) or commonly known as Spam Email, has increased at a tremendous growth rate. We present an analysis of survey based on classifications of UBE in various research works. There are many research instances for classification between spam and non-spam emails but very few research instances are available for classification of spam emails, p...

2008
Slavisa Sarafijanovic Sabrina Perez Jean-Yves Le Boudec

A well-known approach for collaborative spam filtering is to determine which emails belong to the same bulk, e.g. by exploiting their content similarity. This allows, after observing an initial portion of a bulk, for the bulkiness scores to be assigned to the remaining emails from the same bulk. This also allows the individual evidence of spamminess to be joined, if such evidence is generated b...

Journal: :International Journal of Security and Its Applications 2016

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