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Malware detection is an ever-present challenge for all organizationalgatekeepers, who must maintain high detection rates while minimizinginterruptions to the organization's workflow. To improve detection rates,organizations often deploy an ensemble of detectors. While effective, thisapproach is computationally expensive, since every file - even clear-cut cases- needs to be analyzed by all detectors. Moreover, with an ever-increasingnumber of files to process, the use of ensembles may incur unacceptableprocessing times and costs (e.g., cloud resources). In this study, we proposeSPIREL, a reinforcement learning-based method for cost-effective malwaredetection. Our method enables organizations to directly associate costs tocorrect/incorrect classification, computing resources and run-time, and thendynamically establishes a security policy. This security policy is thenimplemented, and for each inspected file, a different set of detectors isassigned and a different detection threshold is set. Our evaluation on twomalware domains- Portable Executable (PE) and Android Application Package(APK)files - shows that SPIREL is both accurate and extremelyresource-efficient: the proposed method either outperforms the best performingbaselines while achieving a modest improvement in efficiency, or reduces therequired running time by ~80% while decreasing the accuracy and F1-score byonly 0.5%. We also show that our approach is both highly transferable acrossdifferent datasets and adaptable to changes in individual detector performance.

Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning