Third case: We make 2 datasets. The training dataset is composed of 750 normal profiles and 1200 worm profiles by sampling 7 worm types, except one worm which is used as unknown worm. The testing dataset is composed of 1700 normal profiles and 2800 worm profiles. We add one unknown/untrained worm-type profiles into the testing dataset (resulting to the total of 8 types of worms). There are 2 output classes which are normal and worm. In this case, we perform 8 experiments where one unknown worm-type is considered at a time. For example, in the first experiment, we make a training dataset without Blaster worm. Then we add the Blaster worm into the testing dataset. In the next experiment, a different worm-type is excluded from the training dataset but is included for testing. After completing these 8 experiments, we find their average detection results.