  Existing malware detection approaches suffer from a simplistic trade-off
between false positive rate (FPR) and true positive rate (TPR) due to a single
tier classification approach, where the two measures adversely affect one
another. The practical implication for malware detection is that FPR must be
kept at an acceptably low level while TPR remains high. To this end, we propose
a two-tiered learning, called ``Echelon", from raw byte data with no need for
hand-crafted features. The first tier locks FPR at a specified target level,
whereas the second tier improves TPR while maintaining the locked FPR. The core
of Echelon lies at extracting activation information of the hidden layers of
first tier model for constructing a stronger second tier model. Echelon is a
framework in that it allows any existing CNN based model to be adapted in both
tiers. We present experimental results of evaluating Echelon by adapting the
state-of-the-art malware detection model ``Malconv" in the first and second
tiers.
