Despite decades of effort to combat spam, unwanted and even malicious emails, such as phish which aim to deceive recipients into disclosing sensitive information, still routinely find their way into one’s mailbox. To be sure, email filters manage to stop a large fraction of spam emails from ever reaching users, but spammers and phishers have mastered the art of filter evasion, or manipulating t...

In the past few years, machine learning and in particular simple Naive Bayes classifiers have proven their value in filtering spam emails. We hereby put Naive Bayes filters to the test, against potentially more elaborate spam filters that will participate in the ceas 2008 challenge. For this purpose, we use the variants of Naive Bayes that have proven more effective in our earlier studies. Furt...

Abstract Spam emails have been traditionally seen as just annoying and unsolicited containing advertisements, but they increasingly include scams, malware or phishing. In order to ensure the security integrity for users, organisations researchers aim develop robust filters spam email detection. Recently, most based on machine learning algorithms published in academic journals report very high p...

Due 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 a...

As email becomes a popular means for communication over the Internet, the problem of receiving unsolicited and undesired emails, called spam or junk mails, severely arises. To filter spam from legitimate emails, automatic classification approaches using text mining techniques are proposed. This kind of approaches, however, often suffers from low recall rate due to the natures of spam, skewed cl...

This paper studies the dynamics of scam hosting infrastructure, with an emphasis on the role of fast-flux service networks. By monitoring changes in DNS records of over 350 distinct spam-advertised domains collected from URLs in 115,000 spam emails received at a large spam sinkhole, we measure the rates and locations of remapping DNS records, and the rates at which “fresh” IP addresses are used...

Many Internet Service Providers (ISPs), anti-virus companies, and enterprise email vendors use Domain Name System-based Blackhole Lists (DNSBLs) to keep track of IP addresses that originate spam, so that future emails sent from these IP addresses can be rejected out-of-hand. DNSBL operators populate blocking lists based on complaints from recipients of spam, who report the IP address of the rel...

Unsolicited commercial email (UCE, spam), scam and phishing emails make up for more than 90% of all emails sent world-wide. Most antispam methods known rely on filtering emails. Meanwhile, browsers also check URLs against blacklists to avoid fraud. However, all those methods are reactive, ergo they are only able to deal with known attack patterns. Some methods are computing intensive, thus requ...

The rapid growth of spam email attacks and the inherent malicious dynamism within those on a range social, personal business activities warrants an intelligent automated anti-spam framework. Attempts like malware propagation, identity theft, sensitive data pilfering, monetary as well reputational damage are sharply increasing, endangering privacy victim. Current solutions that rather incomplete...

A realistic classification model for spam filtering should not only take account of the fact that spam evolves over time, but also that labeling a large number of examples for initial training can be expensive in terms of both time and money. This paper address the problem of separating legitimate emails from unsolicited ones with active and online learning algorithm, using a Support Vector Mac...