<ul><li>Adversarial Training (AT) has been widely applied to harden malware classifiers against adversarial evasive attacks.</li><li>The effectiveness of AT in identifying and strengthening vulnerable areas of the model's decision space while maintaining high performance on clean data remains underexplored.</li><li>Robustness achieved by AT has often been assessed against unrealistic or weak adversarial attacks, negatively affecting performance on clean data.</li><li>Factors such as data, feature representations, classifiers, and robust optimization settings influence the effectiveness of AT.</li></ul>

On the Effectiveness of Adversarial Training on Malware Classifiers

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