Email communication today is a way of working and communicating for most businesses and public in general. Being able to efficiently receive and send emails therefore becomes a must. Spam email detection and removal then becomes a vital process for the successful email communications, security and convenience. This paper describes a novel way of analysing and filtering incoming emails based on ...

Email has become one of the fastest and most economical forms of communication. However, the increase of email users have resulted in the dramatic increase of spam emails during the past few years. In this paper, email data was classified using four different classifiers (Neural Network, SVM classifier, Naïve Bayesian Classifier, and J48 classifier). The experiment was performed based on differ...

We propose a discriminative classifier learning approach to image modeling for spam image identification. We analyze a large number of images extracted from the SpamArchive spam corpora and identify four key spam image properties: color moment, color heterogeneity, conspicuousness, and self-similarity. These properties emerge from a large variety of spam images and are more robust than simply u...

E-mail is the most prevalent methods for correspondence because of its availability, quick message exchange and low sending cost. Spam mail appears as a serious issue influencing this application today's internet. Spam may contain suspicious URL’s, or may ask for financial information as money exchange information or credit card details. Here comes the scope of filtering spam from legitimate em...

Applied linguistics means a wide range of actions which include addressing few language-based problems or solving some concerns. Emails stay in the leading positions for business as well personal use. This popularity grabs interest individuals with malevolent intentions—phishing and spam email assaults. Email filtering mechanisms were developed incessantly to follow unwanted, malicious content ...

This is my final project for CS 539. In this project, I demonstrate the suitability of neural networks for the task of classifying spam emails. I discuss how I was able to attain a classification accuracy of 94.6% through minor changes in network configuration and the momentum alpha parameter, ultimately outperforming existing research on this same dataset.

Today communication has been revolutionized with email and other online communication systems. However, some computer users have abused the technology used to drive these communications, by sending out thousands and thousands of spam emails with little or no purpose other than to increase traffic or decrease bandwidth. With the electronic mail emerging

Spam has become a major problem that is threatening the efficiency of the current email system. Spam is overwhelming the Internet because 1) emails are pushed from senders to receivers without much control from recipients, and 2) the cost for delivering emails is very low. In this paper, we present an anti-spam framework that slows down spammers: by adding delay to email delivery, and by consum...

IP-based blacklist is an effective way to filter spam emails. However, building and maintaining individual IP addresses in the blacklist is difficult, as new malicious hosts continuously appear and their IP addresses may also change over time. To mitigate this problem, researchers have proposed to replace individual IP addresses in the blacklist with IP clusters, e.g., BGP clusters. In this pap...