Spam filtering implementation using open source software / Ahmad Bakhtiar Ibrahim

Spam is a serious problem that has been increasing plaguing users of the Internet. Programs known as spam filters are employ to assist the user in deciding if an email is worth reading or not. This paper is focuses on build the mail server with spam filter and analysis the effectiveness of rule based (Heuristic) and Bayesian filtering method that use in SpamAssassin. The mail server consolidates software which is available in public domain as a powerful and low cost solution for seeking option in fighting spam. The rule based method using set of rules to define the incoming email as spam or not and Bayesian method using probability of incoming mail to define that the mail is belong to either spam or legitimate mail category after its require a training period for SpamAssassin. The combination of both methods show that SpamAssassin is a better spam checking

Email shape analysis

Email has become an integral part of everyday life. Without a second thought we receive bills, bank statements, and sales promotions all to our inbox. Each email has hidden features that can be extracted. In this paper, we present a new mechanism to characterize an email without using content or context called Email Shape Analysis. We explore the applications of the email shape by carrying out a case study; botnet detection and two possible applications: spam filtering, and social-context based finger printing. Our in-depth analysis of botnet detection leads to very high accuracy of tracing templates and spam campaigns. However, when it comes to spam filtering we do not propose new method but rather a complementing method to the already high accuracy Bayesian spam filter. We also look at its ability to classify individual senders in personal email inbox’s

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

Preventing Spam Blogs Using Content Analysis and User Behaviour Model

Spam blog is a subset of blog which contains nothing more than stolen materials and inauthentic text designed to gain profit from various type of advertisements. Splogs have become a nuisance in the blogosphere because it pollutes search engine results and blog update servers. This paper discusses the similarity between spam blogs and email spams and the techniques used to identify them. The paper also propose the development of a prototype blog update server that implements content analysis and user behaviour model to filter splogs before they are indexed into blog search engine

Evaluation of Bayesian Spam Filter and SVM Spam Filter

CD-RO

A Study on: Opinion/Review Spam Detection

The most common mode for consumers to express their level of satisfaction with their purchases is through online ratings, which we can refer as Online Review System. Network analysis has recently gained a lot of attention because of the arrival and the increasing attractiveness of social sites, such as blogs, social networking applications, micro blogging, or customer review sites. Online review systems plays an important part in affecting consumers' actions and decision making, and therefore attracting many spammers to insert fake feedback or reviews in order to manipulate review content and ratings. Malicious users misuse the review website and post untrustworthy, low quality, or sometimes fake opinions, which are referred as Spam Reviews. In this study, we aim at providing an efficient method to identify spam reviews and to filter out the spam content

Stacking classifiers for anti-spam filtering of e-mail

We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or "spam", floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications