The performance of today’s email anti-spam systems is primarily measured by the percentage of false positives (non-spam messages detected as spam) rather than by the percentage of false negatives (real spam messages left unblocked). One reliable anti-spam technique is the Universal Resource Locator (URL)-based filter, which is utilized by most collaborative signature-based filters. URL-based fi...

A new trend in email spam is the emergence of image spam. Although current anti-spam technologies are quite successful in filtering text-based spam emails, the new image spams are substantially more difficult to detect, as they employ a variety of image creation and randomization algorithms. Spam image creation algorithms are designed to defeat well-known vision algorithms such as optical chara...

With the development of the diversification of spam, it raises the difficulties and challenges to content-based spam filtering. To address this problem, this paper firstly introduced the statistical features of Email headers, and then proposed a method to use these features to improve Bayesian anti-spam filter. The selected Email-header features are presented as the fingerprint vectors, and the...

In this paper we show an instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering. The architecture of the learning-based anti-spam filter is based on a tuneable enhanced instance retrieval network able to accurately generalize e-mail representations. The reuse of sim...

Anti-spam technology is developing rapidly in recent years. With the emerging applications of machine learning in diverse fields, researchers as well as manufacturers around the world have attempted a large number of related algorithms to prevent spam. In this paper, we designed an effective anti-spam protection system, SpamCooling, based on the mechanism of active learning and parallel heterog...

VoIP spam will become severe problem preventing from generalization of VoIP service. For the purpose of presenting multi-leveled anti-spit framework, we divided VoIP service domain into three independent domains as an outbound, an intermediary, and an inbound domain. The proposed framework enables administrator to establish anti-spit policies in each domain. In outbound domain, the framework fo...

We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial email, or “spam”, floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the eff...

Most of current Anti-spam techniques, like the Bayesian anti-spam algorithm, primarily use lexical matching for filtering unsolicited bulk E-mails (UBE) and unsolicited commercial E-mails (UCE). However, precision of spam filtering is usually low when the lexical matching algorithms are used in real dynamic environments. For example, an E-mail of refrigerator advertisements is useful for most f...

Background There is a 10-fold increase in mortality among pregnant women with epilepsy, which greatly exceeds the two to three-fold rate of deaths observed in all people with epilepsy. Risks of uncontrolled convulsive seizures in the mother outweigh potential risk of the medication. The levels of anti epileptic drugs (AEDs) have been shown to reduce during pregnancy. EMPiRE is a randomised cont...