Mitigating the Tragedy of the Digital Commons: the Case of Unsolicited Commercial Email

The growth of unsolicited commercial email imposes increasing costs on organizations and causes considerable aggravation on the part of email recipients. A thriving anti-spam industry addresses some of the frustration. Regulation and various economic and technical means are in the works – all aimed at bringing down the flood of unwanted commercial email. This paper contributes to our understanding of the UCE phenomenon by drawing on scholarly work in areas of marketing and resource ownership and use. Adapting the tragedy of the commons to the email context, we identify a causal structure that drives the direct e-marketing industry. Computer simulations indicate that although filtering may be an effective method to curb UCE arriving at individual inboxes, it is likely to increase the aggregate volume, thereby boosting overall costs. We also examine other response mechanisms, including self-regulation, government regulation, and market mechanisms. The analysis advances understanding of the digital commons, the economics of UCE, and has practical implications for the direct e-marketing industrySPAM; Unsolicited Commercial Email (UCE); Tragedy of the Digital Commons; Simulation

Using Uncensored Communication Channels to Divert Spam Traffic

We offer a microeconomic model of the two-sided market for the dominant form of spam: bulk, unsolicited, and commercial advertising email. We adopt an incentive-centered design approach to develop a simple, feasible improvement to the current email system using an uncensored communication channel. Such a channel could be an email folder or account, to which properly tagged commercial solicitations are routed. We characterize the circumstances under which spammers would voluntarily move much of their spam into the open channel, leaving the traditional email channel dominated by person-to-person, non-spam mail. Our method follows from observing that there is a real demand for unsolicited commercial email, so that everyone can be made better off if a channel is provided for spammers to meet spamdemanders. As a bonus, the absence of filtering in an open channel restores to advertisers the incentive to make messages truthful, rather than to disguise them to avoid filters. We show that under certain conditions all email recipients are better off when an open channel is introduced. Only recipients wanting spam will use the open channel enjoying the less disguised messages, and for all recipients the satisfaction associated with desirable mail received increases, and dissatisfaction associated with both undesirable mail received and desirable mail filtered out decreases

Configurazione di filtri nei pi? diffusi client di Posta Elettronica

This document describes how to set up filtering rules in the most popular email client (such as Qualcomm Eudora, Netscape Messenger, Microsoft Outlook and Outlook Express, in Windows, Mac, and Linux Operating systems) in order to reduce problems provoked by Internet spam (i.e. Unsolicited Bulk E-mail or Unsolicited Commercial E-mail)

ANALISIS DAN IMPLEMENTASI SPAM EMAIL FILTERING MENGGUNAKAN VECTOR SPACE MODEL (ANALYSIS AND IMPLEMENTATION OF SPAM EMAIL FILTERING USING VECTOR SPACE MODEL)

ABSTRAKSI: Banyaknya penggunaan internet sebagai media komunikasi, penyebaran berita serta makin banyaknya layanan penyedia email di internet menyebabkan email spam semakin banyak. Hal ini tentu merugikan bagi pengguna email karena harus menghabiskan banyak waktu untuk menghapus email-email spam tersebut dan dapat menyebabkan media penyimpanan pada email server menjadi penuh. Email spam biasanya berisi pesan komersial tentang suatu produk, usaha, atau bahkan pesan tentang pornografi yang tidak diinginkan oleh user. Saat ini sudah banyak teknik spam filtering yang dibuat untuk mengatasi email spam ini, seperti rule based filtering, naïve bayesian filtering dan support vector machine. Kebanyakan dari aplikasi yang menggunakan teknik spam filtering saat ini, seperti Yahoo Mail tidak dapat mengenali pola dari dokumen email, dan menggunakan pencocokan ekspresi reguler, dimana jika terdapat suatu kata yang mengandung spam dalam suatu email, email tersebut difilter. Meskipun pendekatan ini dapat memfilter email spam, namun hal ini dapat menyebabkan email-email penting juga difilter karena mengandung term tersebut.Pada tugas akhir ini telah dirancang dan diimplementasikan suatu perangkat lunak spam email filtering menggunakan salah satu pendekatan teknik information retreival, yang disebut Vector Space Model. Vektor Space Model memperlakukan query sebagai vektor dalam ruang multidimensional. Sekumpulan data indexing berupa email spam dan email legitimate diberikan kepada perangkat lunak spam email filtering ini, sehingga dapat mengkategorisasikan email dengan mengidentifikasi content dari email untuk menentukan email mana yang merupakan spam email.. Sehingga, ketika spam tersebut cocok, maka perangkat lunak ini akan memfilternya.Kata Kunci : spam, email filtering, information retreival, vektor space model.ABSTRACT: Too much using of internet as communication media, news spreading, and there are a lot of email service provider in internet cause the number of spam email being excessively. It surely can harm the email user because the user have to spend much time to delete spam emails and can cause the storage media on email server being full. Spam email is flooding the internet with many copies of the same message, in a attempt to force the message on people who would not choose to receive it. Spam email usualy consist of commercial message to some product, bussiness message, or even porn message on user who would not want it. At present, there are many spam filtering technique that are developed to force this spam email, for example rule base filtering, naive bayesian filtering and support vector machine. Most of email applications that using spam filtering technique, such as Yahoo Mail, can not understand the semantics of email document, and use a regular expression match, where if a term appears in a particular email, it is filtered. Although this approach is able to filter spam emails, it could occasionally filter some important emails, which might just cotain such term.This Final Project has designed and implemented a spam email filtering tool using one of Information Retrieval Technique, called Vector Space Model. Vector Space Model act the query as a vector in mutidimensional room. Given an indexing data of spam and legitimate message, so that the spam email filtering tool is able to categorize email, by indentifying content of email to determine which one is spam email .Thus, whenever spam is match, it is filtered.Keyword: spam, email filtering, information retreival, vektor space model

Vertically-Oriented Graphene Electric Double Layer Capacitor Designs

High-voltage electric double layer capacitors (EDLCs) capable of efficient AC line-filtering have been developed. They were fabricated with vertically-oriented graphene nanosheet (VOGN) electrodes using a planar design. Two approaches were examined to series connect EDLC cells and thus achieve high-voltage operation. Electrical performance of VOGN electric double layer capacitors fabricated with an ionic liquid electrolyte was measured at temperatures up to 125 degrees C. Volume comparisons are made between VOGN electric double layer capacitors and aluminum electrolytic capacitors. A practical design is presented that provides the VOGN electric double layer capacitor with more than an order-of-magnitude higher ripple-current filtering performance (120-Hz) than available from present capacitor technology. (C) The Author(s) 2015. Published by ECS. This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives 4.0 License (CC BY-NC-ND, http://creativecommons.orellicenses/by-nc-ndA.0/), which permits non-commercial reuse, distribution, and reproduction in an); medium, provided the original work is not changed in any way and is properly cited. For permission for commercial reuse, please email: [email protected]. All rights reserved

Single-Class Learning for Spam Filtering: An Ensemble Approach

Spam, also known as Unsolicited Commercial Email (UCE), has been an increasingly annoying problem to individuals and organizations. Most of prior research formulated spam filtering as a classical text categorization task, in which training examples must include both spam emails (positive examples) and legitimate mails (negatives). However, in many spam filtering scenarios, obtaining legitimate emails for training purpose is more difficult than collecting spam and unclassified emails. Hence, it would be more appropriate to construct a classification model for spam filtering from positive (i.e., spam emails) and unlabeled instances only; i.e., training a spam filter without any legitimate emails as negative training examples. Several single-class learning techniques that include PNB and PEBL have been proposed in the literature. However, they incur fundamental limitations when applying to spam filtering. In this study, we propose and develop an ensemble approach, referred to as E2, to address the limitations of PNB and PEBL. Specifically, we follow the two-stage framework of PEBL and extend each stage with an ensemble strategy. Our empirical evaluation results on two spam-filtering corpora suggest that the proposed E2 technique exhibits more stable and reliable performance than its benchmark techniques (i.e., PNB and PEBL)

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