A method is proposed for learning to classify spam and nonspam emails. It combines the strategy of the Best Stepwise Feature Selection with a classifier of Euclidean nearest-neighbor. Each text email is first transformed into a vector of D-dimensional Euclidean space. Emails were divided into training and test sets in the manner of 10-fold crossvalidation. Three experiments were performed, and ...

Malicious software infects large numbers of computers around the world. Once compromised, the computers become part of a botnet and take part in many forms of criminal activity, including the sending of unsolicited commercial email or spam. As mobile devices become tightly integrated with the Internet, associated threats such as botnets have begun to migrate onto the devices. This paper describ...

Spam detection has become a necessity for successful email communications, security and convenience. This paper describes a learning process where the text of incoming emails is analysed and filtered based on the salient features identified. The method described has promising results and at the same time significantly better performance than other statistical and probabilistic methods. The sali...

Email is the number one activity that people do on the internet: 74% of internet users check their email on an average day. Email use in offices has more than doubled since 2000, and is now over 8 hours a week. There are many great NLP problems for email, like automatic clustering and foldering, search, prioritization, automatically finding keywords within messages, finding addresses, and summa...

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable antispam filters. Using a classifier based on machine learning techniques to automatically filter out spam email has drawn many researchers attention. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system...

Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by treating it as a static text classification problem. True in vivo spam filtering has characteristics that make it a rich and challenging domain for data mining. Indeed, real-world datasets with these characteristics a...

As the email service is becoming an important communication way on the Network, the spam is increasing every day. This paper describes a new filtering model based on email content by using Back-Propagation Neural Networks (BPNN). And for the Chinese email, it uses Natural Language Processing & Information Retrieval Sharing Platform (NLPIR) system to perform Chinese word segmentation. The simula...

As the problem of spam email increases, we examined users attitudes toward and experience with spam as a function of gender and age. College-age, working-age, and retirement-age men and women were surveyed. Most respondents strongly disliked receiving spam yet took few actions against it. There were fewer gender differences than predicted, but age was a significant predictor of several response...

Spam, or Unsolicited Bulk Email, is a big problem in nowadays internet. Recent studies report that spam accounts for more than 90% of the worldwide email traffic [40]. Spam is not only annoying for users, who receive content they did not request, but is also a burden for the whole email delivery infrastructure, that needs to keep delivering legitimate emails with a short delays, but also make s...