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3.4.1 Content-based filtering The most widespread approach to fighting unwanted communication is content-based filtering, where recipients have software that uses heuristics to automatically classify communication based on its contents. Popular examples are email filtering systems like SpamAssassin [148] and dSPAM [45]. Since content-based filters are installed at the email receiver, they lend themselves to customization and incremental deploy- ment. Today’s state-of-the-art filters are effective at blocking most spam, with some reporting correct classification of over 99% of messages [45]. Content-based filters are also used for other types of unwanted communication, such as blog spam [103] and network-based security attacks [84]. Content-based filtering, however, is subject to both false positives and false nega- tives. False negatives — that is, when unwanted communication is classified as wanted — are a mere inconvenience. False positives [5] are a much more serious concern, be- cause relevant messages are marked as unwanted and thus may not be received [65]. Moreover, there is a continual arms race [64] between spammers and filter developers, because the cognitive and visual capabilities of humans allow spammers to encode their message in a way that users can recognize but filtering programs have difficulty detecting. When early spam filters looked for certain keywords and text patterns in messages, spammers started to misspell telltale words and include random text to escape detection. Nowadays, spammers embed text in images, requiring spam filters to use optical character recognition (OCR) components. 48PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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