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DOOM: A Novel Adversarial-DRL-Based Op-Code Level Metamorphic Malware Obfuscator for the Enhancement of IDS., , and . CoRR, (2020)Adversarial superiority in android malware detection: Lessons from reinforcement learning based evasion attacks and defenses., , , and . Forensic Sci. Int. Digit. Investig., 44 (Supplement): 301511 (March 2023)Android Malicious Application Classification Using Clustering., , , and . ISDA (2), volume 941 of Advances in Intelligent Systems and Computing, page 659-667. Springer, (2018)Are CNN based Malware Detection Models Robust?: Developing Superior Models using Adversarial Attack and Defense., , , and . SenSys, page 355-356. ACM, (2021)Designing Adversarial Attack and Defence for Robust Android Malware Detection Models., , , and . DSN (Supplements), page 29-32. IEEE, (2021)DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems., , and . CoRR, (2021)Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection., , and . Inf. Syst. Frontiers, 25 (2): 589-611 (April 2023)DRo: A data-scarce mechanism to revolutionize the performance of DL-based Security Systems., , and . LCN, page 581-588. IEEE, (2021)Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering., , , and . CoRR, (2021)Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review., , and . CoRR, (2022)