MailMind is an application of backpropagation networks to the real world problem of sorting e-mail messages based upon the sender’s address. This paper presents the strategies employed in the project to adapt to the greatly varying properties of datasets in this domain, such as scale and time dependent change of the problem as well as extremes in noisiness and lack of variation. These strategie...

E-mail communication still has to cope with certain security problems. The most visible result is the mass of unsolicited messages outnumbering the regular e-mails in magnitudes. The technical reasons for this unfavorable situation are manifold, e.g., unreliable sender authentication, loose and ad-hoc coupling between the involved servers, and only few ways to complain about misbehavior of user...

Email has become an essential communication tool in modern life, creating the need to manage the huge information generated. Email classification is a desirable feature in an email client to manage the email messages and categorize them into semantic groups. Statistical artificial intelligence and machine learning is a typical approach to solve such problem, driven by the success of such method...

AbstrAct In this paper, we propose a framework namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling the misclassified documents. Whenever a user points out misclas-sified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces this learning for future classifications....

Two methods for learning text classifiers are compared on classification problems that might arise in filtering and filing personM e-mail messages: a "traxiitionM IR" method based on TF-IDF weighting, and a new method for learning sets of "keyword-spotting rules" based on the RIPPER rule learning algorithm. It is demonstrated that both methods obtain significant generalizations from a small num...

This work applies data mining techniques to detect e-mail worms. E-mail messages contain a number of different features such as the total number of words in message body/subject, presence/absence of binary attachments, type of attachments, and so on. The goal is to obtain an efficient classification model based on these features. The solution consists of several steps. First, the number of feat...

We discuss a classification-based approach for filtering phishing messages in an e-mail stream. Upon arrival, various features of every e-mail are extracted. This forms the basis of a classification process which detects potentially harmful phishing messages. We introduce various new features for identifying phishing e-mail and rank established as well as newly introduced features according to ...

Traditional pen and paper transactions are becoming more and more replaced by equivalent electronic services. Therefore electronic e-mail should also provide enhanced services as those provided by traditional mail. In this paper we present new optimistic protocols for certified e-mail. The major contribution of our paper is the definition of a new property, specific to certified e-mail: no auth...

Validity and efficiency of e-mail use in business communication have not been disputable for a long time. But what is disputable is security and risks of e -mail use. It is in danger because of threats coming from different sources and forms of work. E-mail content, computer data, corporative networks as well as business processes and image and partner’s trust can also be in danger. This paper ...

Most e-mail readers spend a significant amount of time regularly deleting junk e-mail (spam) messages, which are a part of marketing campaigning efforts of various companies wherein users normally signed in and it also results in increasing volume of storage space and consumes network bandwidth. A challenge, therefore, rests with the developers and improvement of automatic classifiers that can ...