Email Spam Filtering: A Systematic Review

Abstract

Spam is information crafted to be delivered to a large number of recipients, in spite of their wishes. A spam filter is an automated tool to recognize spam so as to prevent its delivery. The purposes of spam and spam filters are diametrically opposed: spam is effective if it evades filters, while a filter is effective if it recognizes spam. The circular nature of these definitions, along with their appeal to the intent of sender and recipient make them difficult to formalize. A typical email user has a working definition no more formal than "I know it when I see it." Yet, current spam filters are remarkably effective, more effective than might be expected given the level of uncertainty and debate over a formal definition of spam, more effective than might be expected given the state-of-the-art information retrieval and machine learning methods for seemingly similar problems. But are they effective enough? Which are better? How might they be improved? Will their effectiveness be compromised by more cleverly crafted spam?We survey current and proposed spam filtering techniques with particular emphasis on how well they work. Our primary focus is spam filtering in email; Similarities and differences with spam filtering in other communication and storage media -such as instant messaging and the Web -are addressed peripherally. In doing so we examine the definition of spam, the user's information requirements and the role of the spam filter as one component of a large and complex information universe. Well-known methods are detailed sufficiently to make the exposition self-contained, however, the focus is on considerations unique to spam. Comparisons, wherever possible, use common evaluation measures, and control for differences in experimental setup. Such comparisons are not easy, as benchmarks, measures, and methods for evaluating spam filters are still evolving. We survey these efforts, their results and their limitations. In spite of recent advances in evaluation methodology, many uncertainties (including widely held but unsubstantiated beliefs) remain as to the effectiveness of spam filtering techniques and as to the validity of spam filter evaluation methods. We outline several uncertainties and propose experimental methods to address them.

Can You Believe an Anonymous Contributor? On Truthfulness in Yahoo! Answers

“…Similarly, 4.3% of "secret" askers choose a nickname that is identical to their Yahoo! identifier, compared to 5.3% of the control group 10 . Thus, "secret" askers are more likely to mask their identifies.…”

“…Social media, like email, is clearly exposed to spam and abuse. Anti-spam techniques have been widely researched both in the context of email [10] and social media [11], with some work focusing on Yahoo! Answers specifically [12].…”

Utilizing Multi-Field Text Features for Efficient Email Spam Filtering

“…The online TC algorithms have shown a good effectiveness for email spam filtering [8]. To our knowledge, the online SVM algorithm indeed gives state of the art performance on email spam filtering, which has out-performed other single TC algorithms, including the perceptron algorithm, the Bayesian algorithm, and the logistic regression algorithm [9].…”

“…The previous TC algorithms often pursued the high classification accuracy and the high overall performance 1-ROCA [8] of online supervised learning, without more claiming their low space-time complexity. For instance, specified in the TREC spam track, even the space-time limitation (total 1 GB RAM and 2 sec/email) is still unpractical and horrible in a real large-scale email system, where large-scale emails will form a round-the-clock data stream and there will be more than thousands of emails arriving during 2 seconds.…”

On the detection and identification of botnets

“…Spam filters have become reasonably effective by now (see, for example, [21,5]). However, new challenges, such as new forms of image spam and audio-based spam may lead to decreasing performance of today's most widely used Bayesian spam filters.…”