We investigate the performance of two machine learning algorithms in the context of antispam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and perform poorly. The Naive Bayesian classifier has recently been suggested as an ...

The effectiveness of current anti-spam systems is limited by the ability of spammers to adapt to filtering techniques and the lack of incentive for mail servers to filter outgoing spam. A new approach, based on decentralised trust management, is described in this paper. An architecture and protocol, called TOPAS (Trust Overlay Protocol for Anti Spam), are presented. Each mail server records tru...

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...

Spam-reduction techniques have developed rapidly over the last few years, as spam volumes have increased. We believe that no one anti-spam solution is the “right” answer, and that the best approach is a multifaceted one, combining various forms of filtering with infrastructure changes, financial changes, legal recourse, and more, to provide a stronger barrier to spam than can be achieved with o...

Over the last decade, unsolicited bulk email—spam— has evolved dramatically in its volume, its delivery infrastructure and its content. Multiple reports indicate that more than 90% of all email traversing the Internet today is considered spam. This growth is partially driven by a multi-billion dollar anti-spam industry whose dedication to filtering spam in turn requires spammers to recruit botn...

We present a thorough investigation on using machine learning to construct effective personalized anti-spam filters. The investigation includes four learning algorithms, Naive Bayes, Flexible Bayes, LogitBoost, and Support Vector Machines, and four datasets, constructed from the mailboxes of different users. We discuss the model and search biases of the learning algorithms, along with worst-cas...

Embedded-Text Detection and Its Application to Anti-Spam Filtering Ching-Tung Wu Embedded-text in images usually carry important messages about the content. In the past, several algorithms have been proposed to detect text boxes in video frames. Previous work often followed a multi-step framework using a combination of image-analysis and machine-learning techniques. In this work, we propose a u...

Web spammers aim to obtain higher ranks for their web pages by including spam contents that deceive search engines in order to include their pages in search results even when they are not related to the search terms. Search engines continue to develop new web spam detection mechanisms, but spammers also aim to improve their tools to evade detection. In this study, we first explore the effect of...

We present Filtron, a prototype anti-spam filter that integrates the main empirical conclusions of our comprehensive analysis on using machine learning to construct effective personalized anti-spam filters. Filtron is based on the experimental results over several design parameters on four publicly available benchmark corpora. After describing Filtron’s architecture, we assess its behavior in r...

In our modern society telephony has developed to an omnipresent service. People are available at anytime and anywhere. Furthermore the Internet has emerged to an important communication medium. These facts and the raising availability of broadband internet access has led to the fusion of these two services. Voice over IP or short VoIP is the keyword, that describes this combination. The advanta...