Combining Text and Heuristics for Cost-Sensitive Spam Filtering

In: Proceedings of CoNLL-2000 and LLL-2000, pages 99-102, Lisbon, Portugal, 2000. Combining Text and Heuristics for Cost-Sensitive Spam Filtering Jos4 M. G6mez Hidalgo Manuel Mafia L6pez* Universidad Europea-CEES, Spain Universidad de Vigo, Spain jmgomez@dinar, es±. uem. es mj lopez@uvigo, es Enrique Puertas Sanz Universidad Europea-CEES, Spain epsilonmail@retemail, es Abstract Spam filtering is a text categorization task that shows especial features that make it interest- ing and difficult. First, the task has been per- formed traditionally using heuristics from the domain. Second, a cost model is required to avoid misclassification of legitimate messages. We present a comparative evaluation of several machine learning algorithms applied to spam fil- tering, considering the text of the messages and a set of heuristics for the task. Cost-oriented biasing and evaluation is performed. 1 Introduction Spam, or more properly Unsolicited Commer- cial E-mail (UCE), is an increasing threat to the viability of Internet E-mail and a danger to Internet commerce. UCE senders take away resources from users and service suppliers with- out compensation and without authorization. A variety of counter-measures to UCE have been proposed, from technical to regulatory (Cranor and LaMacchia, 1998). Among the technical ones, the use of filtering methods is popular and effective. UCE filtering is a text categorization task. Text categorization (TC) is the classification of documents with respect to a set of one or more pre-existing categories. In the case of UCE, the task is to classify e-mail messages or newsgroups articles as UCE or not (that is, legitimate). The general model of TC makes use of a set of pre- classified documents to classify new ones, ac- cording to the text content (i.e. words) of the documents (Sebastiani, 1999). Although UCE filtering seems to be a simple instance of the more general TC task, it shows * Partially supported by the CICYT, project no. TEL99-0335-C04-03 two special characteristics: * First, UCE filtering has been developed us- ing very simple heuristics for many years. For example, one individual could manu- ally build a filter that classifies as "spam" messages containing the phrase "win big money", or with an unusual (big) number of capital letters or non-alphanumeric char- acters. These rules are examples of simple but powerful heuristics that could be used to complement a word-based automatic TC system for UCE filtering. • Second, all UCE filtering errors are not of equal importance. Individuals usually pre- fer conservative filters that tend to classify UCE as legitimate, because missing a le- gitimate message is more harmful than the opposite. A cost model is imperative to avoid the risk of missing legitimate e-mail. Many learning algorithms have been applied to the problem of TC (Yang, 1999), but much less with the problem of UCE filtering in mind. Sahami and others (1998) propose the utiliza- tion of a Naive Bayes classifier based on the words and a set of manually derived heuris- tics for UCE filtering, showing that the heuris- tics improve the effectiveness of the classifier. Druker and others (1999) compare boosting, Support Vector …

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