to the constant increase in the volume of information available to applications in fields varying from medical diagnosis to web search engines, accurate support of similarity becomes an important task. This is also the case of spam filtering techniques where the similarities between the known and incoming messages are the fundaments of making the spam/not spam decision. We present a novel appro...

In this paper we quantify the effect of unsolicited emails (spam) on behavior and engagement of email users. Since performing randomized experiments in this setting is rife with practical and moral issues, we seek to determine causal relationships using observational data, something that is difficult in many cases. Using a novel modification of a user matching method combined with a time series...

There are number of Anti-Spam filters that have reduced the amount of email spam in the inbox but the problem still continues as the spammers circumvent these techniques. The problems need to be addressed from different aspects. Major problem for instance arises when these anti-spam techniques misjudge or misclassify legitimate emails as spam (false positive); or fail to deliver or block spam o...

During the last decade the number of spam messages sent has increased significantly. These undesired emails place a heavy burden on end users and email service providers. As a result, a tenacious struggle to outsmart each other exists between people who send spam and the spam filter providers. Constant innovation is therefore of vital importance to curb the rapid increase of spam. In this artic...

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...

Because of the volume of spam email and its evolving nature, any deployed Machine Learning-based spam filtering system will need to have procedures for case-base maintenance. Key to this will be procedures to edit the case-base to remove noise and eliminate redundancy. In this paper we present a two stage process to do this. We present a new noise reduction algorithm called Blame-Based Noise Re...

Email spam is a major problem for the sustainability of the Internet and global commerce. Every day million of emails are sent across by spammers to targeted population to advertise products, services, spread dangerous software etc. Currently a number of spam detection algorithms have been proposed in the literature to classify email spam. Most of these algorithms can be categorized as metadata...

Most e-mail readers spend a significant amount of time regularly deleting junk e-mail (spam) messages, which are a part of marketing campaigning efforts of various companies wherein users normally signed in and it also results in increasing volume of storage space and consumes network bandwidth. A challenge, therefore, rests with the developers and improvement of automatic classifiers that can ...

With the rapid increase of spam on Internet and diversification its forms, how to quickly effectively identify a large number has become an urgent topic. Cloud computing obvious advantages in storage processing, so it can calculate amount mail data. Due uncertainty life cycle spam, feedback re-judgment is added anti-spam system, text filtering system based active learning with four stages train...

In this paper we present Preventive Spatio-Temporal Aggregation (PRESTA), a reputation model that combines spatial and temporal features to produce values that are behavior predictive and useful in partialknowledge situations. To evaluate its effectiveness, we applied PRESTA in the domain of spam detection. Studying the temporal properties of IP blacklists, we found that 25% of IP addresses onc...