14,491 research outputs found

    System Analysis of SPAM

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    Increasing reliance on the electronic mail (e-mail) has attracted spammers to send more and more spam e-mails in order to maximizing their financial gains. These unwanted e-mails are not only clogging the Internet traffic but are also causing storage problems at the receiving servers. Besides these, spam e-mails also serve as a vehicle to a variety of online crimes and abuses. Although several anti-spam procedures are currently employed to distinguish spam e-mails from the legitimate e-mails yet spammers and phishes obfuscate their e-mail content to circumvent anti-spam procedures. Efficiency of anti-spam procedures to combat spam entry into the system greatly depend on their level of operation and a clear insight of various possible modes of spamming. In this paper we investigate directed graph model of Internet e-mail infrastructure and spamming modes used by spammers to inject spam into the system. The paper outlines the routes, system components, devices and protocols exploited by each spamming mode

    A collaborative approach for spam detection

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    Electronic mail is nowadays one of the most important Internet networking services. However, there are still many challenges that should be faced in order to provide a better e-mail service quality, such as the growing dissemination of unsolicited e-mail (spam) over the Internet. This work aims to foster new research efforts giving ground to the development of novel collaborative approaches to deal with spam proliferation. Using the proposed system, which is able to complement other anti-spam solutions, end-users are allowed to share and combine spam filters in a flexible way, increasing the accuracy and resilience levels of anti-spam techniques.(undefined

    Forgery in Cyberspace: The Spoof Could Be on You!

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    Spoofing is one of the newest forms of cyber-attack, a technological methodology adapted to mask the identity of spammers who have faced hostile reaction in response to bulk, unsolicited, electronic mail messages.[1] Sending Spam, however, is no longer the only reason for deception, as crackers have taken pleasure in the challenge of manipulating computer systems and, additionally, find recreational enjoyment in doing so. In this legal Note, the author’s intent is to show that criminal, rather than civil liability is the best way to effectively deter and punish the spoofer. The injury that results when a computer system’s technological safety measures fail to adequately safeguard the system affects not only the owner of the hijacked e-mail address, but also the Internet Service Provider, and the Network as a whole. Current Anti-Spam Legislation is arguably ineffective at targeting these particular types of malicious attacks, and a different legal approach is suggested

    SpamHunting: An instance-based reasoning system for spam labelling and filtering

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    n this paper we show an instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering. The architecture of the learning-based anti-spam filter is based on a tuneable en-hanced instance retrieval network able to accurately generalize e-mail representations. The reuse of similar messages is carried out by a simple unanimous voting mechanism to determine whether the tar-get case is spam or not. Previous to the final response of the system, the revision stage is only performed when the assigned class is spam whereby the system employs general knowledge in the form of meta-rules

    Review on Effective Email Classification for Spam and Non Spam Detection on Various Machine Learning Techniques

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    Some time email receiver or user receives a email which he does not intended to receive or accept, these kind of emails are nothing but spam emails. In other words the unsolicited bulk email is nothing but the spam. Numbers of emails users are increasing day by day, email users communicate around the world using email and internet. Now days a large volumes of spam emails are causing serious problem for Internet service and Internet users. This affects or degrades user search experience, which assists propagation of virus in network or grid, this will increases load on traffic in the network. It also wastes valuable time of user, user’s energy for appropriate emails among the spam emails. To avoiding such spam there are so many traditional anti spam techniques includes, rule based system, White list and DNS black holes, IP blacklist, Heuristic based filter, Bayesian based filters. All these techniques are based on links of the mail or content of the email. In this paper, we conferred our study on various existing techniques on spam detection and finding the effective, accurate, and reliable spam detection technique. DOI: 10.17762/ijritcc2321-8169.150315

    Desenvolvimento de um sistema Anti-Spam de Código Aberto

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    In this work, we present the development of an Open Source Anti-Spam System (SASCA) in Java. Unlike commercial anti-spam systems, SASCA does not make use of block lists (black / white), but rather of m machine learning models for email classification. Several experiments were carried out on a basis of real e-mails collected at the Federal University of Itajubá. In the experiments carried out, it was observed that the SASCA performed very close to the CanIt commercial anti-spam system in terms of e-mail classification, but with a much better performance in terms of the time required for classification.Neste trabalho, apresentamos o desenvolvimento de um Sistema Anti-Spam de Código Aberto (SASCA), em Java. Ao contrário de sistemas anti-spam comerciais, o SASCA não faz uso de listas de bloqueio (negras/brancas) e sim de modelos de machine learning para classificação de e-mails. Foram realizados diversos experimentos sobre uma base de e-mails reais, coletados na Universidade Federal de Itajubá Nos experimentos realizados, observou-se que o SASCA obteve desempenho bem próximo ao do sistema anti-spam comercial CanIt, em termos de classificação de e-mails, mas com desempenho bem melhor, em termos de tempo requerido para classificação

    Minimizing the Time of Spam Mail Detection by Relocating Filtering System to the Sender Mail Server

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    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
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