Unsolicited emails, known as spam, are one of the fast growing and costly problems associated with the Internet today. Among the many proposed solutions, a technique using Bayesian filtering is considered as the most effective weapon against spam. Bayesian filtering works by evaluating the probability of different words appearing in legitimate and spam mails and then classifying them based on t...

By now email has become an indispensable means of communication for mankind. However, during the past few years, it has been hijacked by spam. A resounding majority of email users are very much annoyed by their daily struggle with spam. They are forced to glance through a large number of unwanted, misleading, or offensive emails from out of nowhere; they have to purge from the inbox and deleted...

Recently, email has become a common way for people to communicate and share information both officially personally. Email may be used by spammers transmit harmful materials Internet users. The data must protected from unauthorized access, which necessitates the development of reliable method identifying spam emails. As result, variety solutions have been devised. An innovative hybrid machine le...

This paper proposes a novel behavior-based anti-spam technology for email service based on an artificial immune-inspired clustering algorithm. The suggested method is capable of continuously delivering the most relevant spam emails from the collection of all spam emails that are reported by the members of the network. Mail servers could implement the anti-spam technology by using the “black lis...

Fast analysis of correlated spam emails may be vital in the effort of finding and prosecuting spammers performing cybercrimes such as phishing and online frauds. This paper presents a self-learning framework to automatically divide and classify large amounts of spam emails in correlated labeled groups. Building on large datasets daily collected through honeypots, the emails are firstly divided ...

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...

The problem of spam e-mail has gained a tremendous amount of attention. Although entities tend to use e-mail spam filter applications to filter out received spam e-mails, marketing companies still tend to send unsolicited emails in bulk and users still receive a reasonable amount of spam e-mail despite those filtering applications. This work proposes a new method for classifying emails into spa...

This paper attempts to develop an algorithm to recognize spam domains using data mining techniques with the focus on law enforcement forensic analysis. Spam filtering has been the major weapon against spam, but failed to reduce the number of spam emails sent to an indiscriminate set of recipients. The proposed algorithm accepts as input, spam mails of personal account and extracts features such...

The battle between email service providers and senders of mass unsolicited emails (Spam) continues to gain traction. Vast numbers of Spam emails are sent mainly from automatic botnets distributed over the world. One method for mitigating Spam in a computationally efficient manner is fast and accurate blacklisting of the senders. In this work we propose a new sender reputation mechanism that is ...