Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms
This work is significant for cybersecurity professionals and organizations that need to detect malware with high accuracy while minimizing false alarms, which can be costly and disruptive.
This paper proposes Echelon, a two-tiered malware detection system for raw executables, to address the trade-off between false positive rate (FPR) and true positive rate (TPR) in single-tier classification. The first tier aims to lock the FPR at a specified low level, while the second tier improves TPR without increasing the FPR.
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.