427 research outputs found
Minimizing the Time of Spam Mail Detection by Relocating Filtering System to the Sender Mail Server
Unsolicited Bulk Emails (also known as Spam) are undesirable emails sent to
massive number of users. Spam emails consume the network resources and cause
lots of security uncertainties. As we studied, the location where the spam
filter operates in is an important parameter to preserve network resources.
Although there are many different methods to block spam emails, most of program
developers only intend to block spam emails from being delivered to their
clients. In this paper, we will introduce a new and efficient approach to
prevent spam emails from being transferred. The result shows that if we focus
on developing a filtering method for spams emails in the sender mail server
rather than the receiver mail server, we can detect the spam emails in the
shortest time consequently to avoid wasting network resources.Comment: 10 pages, 7 figure
Analyzing the Social Structure and Dynamics of E-mail and Spam in Massive Backbone Internet Traffic
E-mail is probably the most popular application on the Internet, with
everyday business and personal communications dependent on it. Spam or
unsolicited e-mail has been estimated to cost businesses significant amounts of
money. However, our understanding of the network-level behavior of legitimate
e-mail traffic and how it differs from spam traffic is limited. In this study,
we have passively captured SMTP packets from a 10 Gbit/s Internet backbone link
to construct a social network of e-mail users based on their exchanged e-mails.
The focus of this paper is on the graph metrics indicating various structural
properties of e-mail networks and how they evolve over time. This study also
looks into the differences in the structural and temporal characteristics of
spam and non-spam networks. Our analysis on the collected data allows us to
show several differences between the behavior of spam and legitimate e-mail
traffic, which can help us to understand the behavior of spammers and give us
the knowledge to statistically model spam traffic on the network-level in order
to complement current spam detection techniques.Comment: 15 pages, 20 figures, technical repor
Evaluation of Email Spam Detection Techniques
Email has become a vital form of communication among individuals and organizations in today’s world. However, simultaneously it became a threat to many users in the form of spam emails which are also referred as junk/unsolicited emails. Most of the spam emails received by the users are in the form of commercial advertising, which usually carry computer viruses without any notifications. Today, 95% of the email messages across the world are believed to be spam, therefore it is essential to develop spam detection techniques. There are different techniques to detect and filter the spam emails, but off recently all the developed techniques are being implemented successfully to minimize the threats. This paper describes how the current spam email detection approaches are determining and evaluating the problems. There are different types of techniques developed based on Reputation, Origin, Words, Multimedia, Textual, Community, Rules, Hybrid, Machine learning, Fingerprint, Social networks, Protocols, Traffic analysis, OCR techniques, Low-level features, and many other techniques. All these filtering techniques are developed to detect and evaluate spam emails. Along with classification of the email messages into spam or ham, this paper also demonstrates the effectiveness and accuracy of the spam detection techniques
Design and Implementation of a DMARC Verification Result Notification System
Damages caused by spoofed e-mails as sent from a bank, a public organization and so on become serious social problems. In such e-mails attackers forge the sender address to defraud receivers of their personal and/or secret information. As a countermeasure against spoofed e-mails, sender domain authentication methods such as SPF and DKIM are frequently utilized. However, since most spoofed e-mails do not include DKIM signature in their e-mail header, those e-mails cannot be authenticated by the conventional system. Additionally DKIM has a problem that cannot determine whether the attached signature is legitimate. In this paper, we propose a method to detect spoofed e-mails and alert the user without DKIM signature by utilizing DMARC and implement a system that sends DMARC verification results to receivers. By utilizing this system, the users can obtain alerts for spoofed e-mails that the existing systems cannot warn
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