based spam email filters available and also in operation. Yet we are still inundated with spam emails everyday. This is not because the filters are not powerful enough, but because the filtering systems are not flexible enough to adapt the new development of spam techniques, such as HTML tagging, image based spam, and keyword obfuscating etc. In this paper, we propose to use dynamic multiple no...

Hout, Goldinger, and Ferguson (2013) critically examined the spatial arrangement method (SpAM), originally proposed by Goldstone (1994), as a fast and efficient way to collect similarity data for multidimensional scaling. We found that SpAM produced high-quality data, making it an intuitive and user-friendly alternative to the classic "pairwise" method. Verheyen, Voorspoels, Vanpaemel, and Stor...

This paper initializes a two element concentration vector as a feature vector for classification and spam detection. Negative selection algorithm proposed by the immune system in solving problems in spam detection is used to distinguish spam from non-spam (self from non-self). Self concentration and non-self concentration are generated to form two element concentration vectors. In this approach...

The massive increase of spam is posing a very serious threat to email which has become an important means of communication. Not only does it annoy users, but it also consumes much of the bandwidth of the Internet. Most spam filters in existence are based on the content of email one way or the other. While these anti-spam tools have proven very useful, they do not prevent the bandwidth from bein...

Proliferation of unrestricted Internet access has brought the community unsolicited commercial email, better known as spam. Underestimated for quite some time, spam is now recognized as a problem costing the community billions of dollars per annum. One of the direct impacts of the spam flood is the widespread deployment of anti-spam measures, such as email filters and block lists. In this paper...

Today, internet users are increases Spam mail is the major problem and big challenges for researcher to reduce it .Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. This paper shows classification of spam mail and solving various problems is related to web space. Many machine learning algorith...

Unsolicited Bulk Emails (also known as Spam) are undesirable emails sent to massive number of users. Spam emails consume the network resources and cause lots of security uncertainties. As we studied, the location where the spam filter operates in is an important parameter to preserve network resources. Although there are many different methods to block spam emails, most of program developers on...

An efficient anti-spam filter that would block all unsolicited messages i.e. spam, without blocking any legitimate messages is a growing need. To address this problem, this report takes a statistically-based approach, employing a Bayesian anti-spam filter, because it is content-based and self-learning (adaptive) in nature. We train the filter, using a large corpus of legitimate messages and spa...

Spam web pages intend to achieve higher-than-deserved ranking by various techniques. While human experts could easily identify spam web pages, the manual evaluating process of a large number of pages is still time consuming and cost consuming. To assist manual evaluation, we propose an algorithm to assign spam values to web pages and semi-automatically select potential spam web pages. We first ...

BadRank is a method for detecting spam web sites, based on the premise that a page is spam if it points to another spam page; i.e., the BadRank score of a page is the weighted sum of the BadRank scores of the pages that it links to. BadRank is an important tool in spam detection. We consider the mathematical structure of BadRank, showing how it can be modified to guarantee that the iterates con...