Unsolicited emails, popularly referred to as spam, have remained one of the biggest threats cybersecurity globally. More than half emails sent in 2021 were resulting huge financial losses. The tenacity and perpetual presence adversary, spammer, has necessitated need for improved efforts at filtering spam. This study, therefore, developed baseline models random forest extreme gradient boost (XGB...

Artificial immune systems (AIS) use the concepts and algorithms inspired by the theory of how the human immune system works. This document presents the design and initial evaluation of a new artificial immune system for collaborative spam filtering. Collaborative spam filtering allows for the detection of not-previously-seen spam content, by exploiting its bulkiness. Our system uses two novel a...

Many classification techniques used for identifying spam emails, treat spam filtering as a binary classification problem. That is, the incoming email is either spam or non-spam. This treatment is more for mathematical simplicity other than reflecting the true state of nature. In this paper, we introduce a three-way decision approach to spam filtering based on Bayesian decision theory, which pro...

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 ...

This paper examines an approach to spam mitigation that rate limits incoming TCP/IP connections to an SMTP server based on the real-time detection of spam within the SMTP message exchange. Our approach is motivated by a desire to cause increased resource consumption at the spammer end of each SMTP connection, and to avoid the negative impact of falsepositives by eventually allowing all emails t...

The A theory of user expectation of system interaction is introduced in the context of User Adapted Interfaces. The usability of an intelligent email client that learns to filter spam emails is tested under three variants of adaptation: no user modeling, user modeling with fixed (optimal) spam cut-offs, and user modeling with user adjustable spam cut-offs. The results supported our hypothesis t...

We present an approach to email filtering based on one-class Information Bottleneck (IB) method in small training sets. When themes of emails are changing continually, the available training set which is high-relevant to the current theme will be small. Hence, we further show how to estimate the learning algorithm and how to filter the spam in the small training sets. First, In order to preserv...