Evaluating Rule-Based and Statistical Filters to Detecting Arabic E-Mail Alert Messages

Abstract

Detecting and filtering e-mail alerts that are related to criminal or terrorist activities is of great interest for both security agencies and people. This paper evaluates and compares the performance of both the rule-based filter and Paul Graham statistical filter for detecting alerts in Arabic e-mail messages. To evaluate the two filters, a set of 1500 Arabic messages related to criminal activities were collected manually from some news websites such as Al-Jazeera Net and BBC Arabic news. The e-mails have been preprocessed, normalized, and then the relevant features were extracted from the collected e-mails by involving categorical proportional difference (CPD) and term frequency variance (TFV) as features weighting methods for the rule-based filter. To test the performance of the two filters, several experiments have been conducted and the result show that the Paul Graham statistical filter was more accurate. It was able to detect about 85% of the e-mail alerts used in the experiments. The rule-based filter has achieved 80% accuracy using the CPD method and 70% accuracy using the TFV method.