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Many challenging real-world problems require the deployment of ensemblesmultiple complementary learning models to reach acceptable performance levels.While effective, applying the entire ensemble to every sample is costly andoften unnecessary. Deep Reinforcement Learning (DRL) offers a cost-effectivealternative, where detectors are dynamically chosen based on the output oftheir predecessors, with their usefulness weighted against their computationalcost. Despite their potential, DRL-based solutions are not widely used in thiscapacity, partly due to the difficulties in configuring the reward function foreach new task, the unpredictable reactions of the DRL agent to changes in thedata, and the inability to use common performance metrics (e.g., TPR/FPR) toguide the algorithm's performance. In this study we propose methods forfine-tuning and calibrating DRL-based policies so that they can meet multipleperformance goals. Moreover, we present a method for transferring effectivesecurity policies from one dataset to another. Finally, we demonstrate that ourapproach is highly robust against adversarial attacks.

A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection