Let Your CyberAlter Ego Share Information and Manage Spam

Almost all of us have multiple cyberspace identities, and these {\em cyber}alter egos are networked together to form a vast cyberspace social network. This network is distinct from the world-wide-web (WWW), which is being queried and mined to the tune of billions of dollars everyday, and until recently, has gone largely unexplored. Empirically, the cyberspace social networks have been found to possess many of the same complex features that characterize its real counterparts, including scale-free degree distributions, low diameter, and extensive connectivity. We show that these topological features make the latent networks particularly suitable for explorations and management via local-only messaging protocols. {\em Cyber}alter egos can communicate via their direct links (i.e., using only their own address books) and set up a highly decentralized and scalable message passing network that can allow large-scale sharing of information and data. As one particular example of such collaborative systems, we provide a design of a spam filtering system, and our large-scale simulations show that the system achieves a spam detection rate close to 100%, while the false positive rate is kept around zero. This system has several advantages over other recent proposals (i) It uses an already existing network, created by the same social dynamics that govern our daily lives, and no dedicated peer-to-peer (P2P) systems or centralized server-based systems need be constructed; (ii) It utilizes a percolation search algorithm that makes the query-generated traffic scalable; (iii) The network has a built in trust system (just as in social networks) that can be used to thwart malicious attacks; iv) It can be implemented right now as a plugin to popular email programs, such as MS Outlook, Eudora, and Sendmail.Comment: 13 pages, 10 figure

Spam on the Internet: can it be eradicated or is it here to stay?

A discussion of the rise in unsolicited bulk e-mail, its effect on tertiary education, and some of the methods being used or developed to combat it. Includes an examination of block listing, protocol change, economic and computational solutions, e-mail aliasing, sender warranted e-mail, collaborative filtering, rule-based and statistical solutions, and legislation

An ontology enhanced parallel SVM for scalable spam filter training

This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart

Symbiotic data mining for personalized spam filtering

Unsolicited e-mail (spam) is a severe problem due to intrusion of privacy, online fraud, viruses and time spent reading unwanted messages. To solve this issue, Collaborative Filtering (CF) and Content-Based Filtering (CBF) solutions have been adopted. We propose a new CBF-CF hybrid approach called Symbiotic Data Mining (SDM), which aims at aggregating distinct local filters in order to improve filtering at a personalized level using collaboration while preserving privacy. We apply SDM to spam e-mail detection and compare it with a local CBF filter (i.e. Naive Bayes). Several experiments were conducted by using a novel corpus based on the well known Enron datasets mixed with recent spam. The results show that the symbiotic strategy is competitive in performance when compared to CBF and also more robust to contamination attacks.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EIA/64541/2006

Symbiotic filtering for spam email detection

This paper presents a novel spam filtering technique called Symbiotic Filtering (SF) that aggregates distinct local filters from several users to improve the overall perfor- mance of spam detection. SF is an hybrid approach combining some features from both Collaborative (CF) and Content-Based Filtering (CBF). It allows for the use of social networks to personalize and tailor the set of filters that serve as input to the filtering. A comparison is performed against the commonly used Naive Bayes CBF algorithm. Several experiments were held with the well-known Enron data, under both fixed and incremental symbiotic groups. We show that our system is competitive in performance and is robust against both dictionary and focused con- tamination attacks. Moreover, it can be implemented and deployed with few effort and low communication costs, while assuring privacy.Fundação para a Ciência e a Tecnologia (FCT) - bolsa PTDC/EIA/64541/200

Towards symbiotic spam e-mail filtering

This position paper discusses the use of symbiotic filtering, a novel distributed data mining approach that combines contentbased and collaborative filtering for spam detection