We present an approach to textual classification based on the suffix tree data structure and apply it to spam filtering. A method for scoring of documents using the suffix tree is developed and a number of scoring and score normalisation functions are tested. Our results show that the character

Unsolicited bulk email (aka. spam) is a major problem on the Internet. To counter spam, several techniques, ranging from spam filters to mail protocol extensions like hashcash, have been proposed. In this paper we investigate the effectiveness of several spam filtering techniques and technologies

Abstract — In this paper, the system that classified spam mail and other mail(regular mail) was constructed by two filters with Bayesian theory and SVM(Support Vector Machine) used well by the text classification task as a text classification algorithm. It was confirmed to evaluate the performance

The efforts of anti-spammers and spammers has often been described as an arms race. As we devise new ways to stem the flood of bulk mail, spammers respond by working their way around the new mechanisms. Their attempts to bypass spam filters illustrates this struggle. Spammers have tried many

Abstract- Nowadays, email plays a significant role in communication and it is being preferred by everyone. As the spam problem grew larger, the interest in spam filters grew accordingly. Different techniques are present to deal with spam filtering. In this paper, a new algorithm based

Email is a private medium of communication, and the in-herent privacy constraints form a major obstacle in devel-oping effective spam filtering methods which require access to a large amount of email data belonging to multiple users. To mitigate this problem, we envision a privacy preserv-ing spam

A challenge problem for KDD 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

Instead of the “bag-of-words ” representation, in the quantitative profile approach to spam filtering and email categorization, an email is represented by an m-dimensional vector of numbers, with m fixed in advance. Inspired by email shape analysis proposed recently by Sroufe et al., two instances

Abstract—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

Instead of the “bag-of-words ” representation, in the quantitative profile approach to spam filtering and email categorization, an email is represented by an m-dimensional vector of numbers, with m fixed in advance. Inspired by Sroufe et al. [Sroufe, P., Phithakkitnukoon, S., Dantu, R