#Drift max pro spam email how to#

Nowadays, the security of information systems is relevant as user information is constantly being shared and moving in the cloud however, there are still many problems such as, unsecured web interfaces, weak authentication, insecure networks, lack of encryption, among others, that make services insecure. The number of devices connected to a home or enterprise network increases every day. Security and privacy concerns are challenging the way users interact with devices. We find feature-free approaches to have lower error rates than their feature-based equivalents. In the face of concept drift, the feature-based approach requires the case base to be rebuilt periodically so that we can select a new feature set that better predicts the target concept. Finally, we compare a feature-based textual case-based spam filter with our feature-free ap- proach. We then compare two different retention policies and two different forgetting policies (one a form of instance selection, the other a form of instance weighting) and find that they perform roughly as well as each other while keeping the case base size constant. We show that a policy as simple as retaining misclassified examples has a hugely beneficial effect on handling concept drift in spam but, on its own, it results in the case base growing by over 30%. In our experiments, we compare two ways to normalise such a distance measure, finding that the one proposed in (1) performs better.

#Drift max pro spam email how to#

In particular, we evaluate how to track concept drift using a case-based spam filter that uses a feature- free distance measure based on text compression. In this paper, we compare case-based spam filters, focusing on their resilience to concept drift.