Stability and Effective Process Control for Secure Email Filtering

A fantastic tool for both commercial and personal communication is electronic mail. It has increasingly become a necessary component of our working life since it is straightforward, available, and simple to use. Spam emails have started to tarnish internet experiences and threaten the integrity of email. Due to the exponential growth of spam, both people and organisations are under a great deal of financial and other strain. In order to prevent the future of email itself from being in jeopardy, a solution to the spam problem must be discovered. There is an urgent need to solve the Email spam issue since spam volume has been rising over the last several decades. As part of this effort, many effects of spam emails on businesses and people were noted and thoroughly examined. In order to properly assess current technologies, solutions, and methods, a comprehensive literature review was conducted throughout the procedures. The goals of this work is to develop new methodologies for the implementation of new strategies for the efficient management of email spam and to construct a proof-of-concept software system for the Process controlled assessment of such strategies

Digital Waste Sorting: A Goal-Based, Self-Learning Approach to Label Spam Email Campaigns

Fast analysis of correlated spam emails may be vital in the effort of finding and prosecuting spammers performing cybercrimes such as phishing and online frauds. This paper presents a self-learning framework to automatically divide and classify large amounts of spam emails in correlated labeled groups. Building on large datasets daily collected through honeypots, the emails are firstly divided into homogeneous groups of similar messages campaigns), which can be related to a specific spammer. Each campaign is then associated to a class which specifies the goal of the spammer, i.e. phishing, advertisement, etc. The proposed framework exploits a categorical clustering algorithm to group similar emails, and a classifier to subsequently label each email group. The main advantage of the proposed framework is that it can be used on large spam emails datasets, for which no prior knowledge is provided. The approach has been tested on more than 3200 real and recent spam emails, divided in more than 60 campaigns, reporting a classification accuracy of 97% on the classified data.pringer International Publishing Switzerland 2015 S. Foresti (Ed.): STM 2015, LNCS 9331, pp. 3?19, 2015. DOI: 10.1007/978-3-319-24858-5