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

Anti-Spam: Reinventing Data

Today, where information is continually transferred in the form of data, the word “information” has all but been exchanged for the word “data.” This shift of terms has aided in effectively transforming the world into a network-world of data. In many areas, and for many professionals, condensing information has become an almost exclusive preoccupation. This need to condense information through selecting and summarizing events—via the use of statistics, infography, visualization software, reports, databases, and animations—has dominated our mental landscape; it dominates the way we structure our perception of reality. Therefore, it is important to rethink what this phenomenon represents and how artists are responding to it. In this network-world of data spam (which is unsolicited e-mail or electronic data sent en mass) has become one of the symbols representing the flux of disinformation, and/or unsolicited, information. Anti-spam is, therefore, a method of eliminating and screening the source data, a tool I call impedance. If we apply this point of view to contemporary art, we could consider the works of Pavel Braila, R. Luke DuBois and André Sier as anti-spam filters that allow the detection, screening, elimination, and subsequent reinvention of existing or non-existent data. In this essay, I propose considering the fundamental aspects of data mining, data visualization, projection mapping. From such considerations emerges the ability to generate, process, and recreate data from the work of these three artists. Finally, I introduce the perspective of the artist as a data miner, this reinvents its source in the new visual, social and political datascape.publishedVersio

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

"May I borrow Your Filter?" Exchanging Filters to Combat Spam in a Community

Leveraging social networks in computer systems can be effective in dealing with a number of trust and security issues. Spam is one such issue where the "wisdom of crowds" can be harnessed by mining the collective knowledge of ordinary individuals. In this paper, we present a mechanism through which members of a virtual community can exchange information to combat spam. Previous attempts at collaborative spam filtering have concentrated on digest-based indexing techniques to share digests or fingerprints of emails that are known to be spam. We take a different approach and allow users to share their spam filters instead, thus dramatically reducing the amount of traffic generated in the network. The resultant diversity in the filters and cooperation in a community allows it to respond to spam in an autonomic fashion. As a test case for exchanging filters we use the popular SpamAssassin spam filtering software and show that exchanging spam filters provides an alternative method to improve spam filtering performance