1. Spam Email Detection Ethan Grefe December 13, 2013

2. Motivation • Spam email is constantly cluttering inboxes • Commonly removed using rule based filters • Spam often has very similar characteristics • This allows them to be detected using machine learning • Naïve Bayes Classifiers • Support Vector Machines

3. SVM Solution • Used training data from CSDMC2010 SPAM corpus • 4327 labeled emails • 2949 non-spam messages (HAM) • 1378 spam messages (SPAM). • Extracted features from the subject and body of emails • Used resulting feature vectors to train an SVM classifier in Matlab

4. Email Features • Features were determined by research and observation • Best results were obtained with the following features • Percentage of letters that arecapitalized • Types of punctuation used • Average length ofa word • Amount of html in the email

5. Classifier Results • Trained on a random 35% of emails • Tested SVM classifier on remaining 65% • Trained SVM using three different kernel functions

6. Possible Improvements • Use Naïve Bayes to classify emails using word frequency • Obtain a wider variety of input features • Test other types of learning algorithms