Computer aided mail filtering using SVM
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1 Computer aided mail filtering using SVM Lin Liao, Jochen Jaeger Department of Computer Science & Engineering University of Washington, Seattle
2 Introduction What is SPAM? Electronic version of junk mail, bulk-mail, unwanted messages Unsolicited Commercial (UCE), Unsolicited Bulk (UBE) Some impacts Annoying unsolicited advertisement or even harmful, offending content Waste of resources (time and bandwidth), ~1Mio per day Threat to the viability of and Internet commerce Reasons for the proliferation of spam Efficiency and increasing popularity of , prone to misuse Very low cost for distribution with relatively high number of responses Trading of private addresses has become as business 2
3 Filtering Typical counter-measures Either technical or regulatory several bills and lawsuits had limited effect Anti-spam filters Automatically identify/classify incoming messages Accept legitimate messages and reject spam Several approaches Blacklist of spam sender-addresses forged headers, faked addresses Simple rule-based solutions, hand-crafted key-word patterns Perform poorly, require frequent updates and fine-tuning More sophisticated solutions take advantage of machine learning Adaptive, user-specific, improves with experience 3
4 Mail Filtering Using SVM Support Vector Machines (SVM) for mail filtering Outperforms other techniques: Rule-based learner, Decision Trees, Naïve Bayesian Scenario Training mode: Analyze test set of marked (classified) mails Extract features, build feature database with number of occurrences for each class Feature Selection select most decisive (unbalanced) features from database Build feature vectors for each message project message into feature space Use SVM to learn feature characteristics and build support vectors Classify incoming mails using SVM Regularly update feature database with new messages Repeat feature selection and apply SVM again 4
5 Analyzing Mail Traditional text classification Each word is a feature, each document is a binary feature vector Special text categorization suggests several extensions Integer value vectors for number of occurrences Different types of features, e.g. Single words and pairs of words, characters body content Domain-specific, non-phrasal features, mostly header information Addresses: Resolved familiar addresses, domain, domain type Content-type, attachments, priority, time zone, receive path Percentage of non-alphanumerical characters and capital letters Feature data structure Type of the feature, string value discretize non-string values 5
6 SVM classifier Find separating hyperplane with max distance to closest training example Advantage: avoids overfitting We used the libsvm implementation for Java 6
7 Result 1 Prediction accuracy for different feature selection methods % Number features % 94.00% 91.00% Accuracy 88.00% 85.00% chi2 chi2n 82.00% 79.00% 76.00% 73.00% 7
8 Results, top 30 features for chi 2 From Subject To Body T: 5 F: 295 : W: '.edu' T: 50 F: 283 : W: 're' T: 14 F: 245 : W: '.edu' T: 13 F: 236 : W: 'cs.washington.edu' T: 54 F: 290 : W: 'jochen' T: 35 F: 15 : W: 'text/html' T: 10 F: 200 : W: 'jochen jaeger' T: 12 F: 195 : W: 'jj@cs.washington.edu' T: 56 F: 42 : W: '#' T: 56 F: 29 : W: '"' T: 4 F: 151 : W: 'cs.washington.edu' T: 56 F: 20 : W: '%' T: 35 F: 415 : W: 'text/plain' T: 54 F: 4 : W: 'click' T: 56 F: 67 : W: '&' T: 54 F: 123 : W: 'seattle' T: 54 F: 246 : W: 'me' T: 54 F: 1 : W: 'removed' T: 55 F: 133 : W: 'jochen jaeger' T: 52 F: 28 : W: '!' T: 54 F: 0 : W: 'unsubscribe' T: 54 F: 219 : W: 'my' T: 54 F: 121 : W: 'washington' T: 55 F: 1 : W: 'be removed' T: 54 F: 12 : W: 'offer' T: 54 F: 147 : W: 'thanks' T: 55 F: 0 : W: 'to unsubscribe' T: 54 F: 99 : W: 'wrote' T: 54 F: 247 : W: 'but' T: 54 F: 182 : W: 'hi' 8 Content type chars
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