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Identification of Adversarial Android Intents using Reinforcement Learning.

, , , and . IJCNN, page 1-8. IEEE, (2021)

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Malware Detection using Machine Learning and Deep Learning., , , and . CoRR, (2019)Identification of Significant Permissions for Efficient Android Malware Detection., , , and . CoRR, (2021)Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review., , and . CoRR, (2022)Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering., , , and . CoRR, (2021)Designing Adversarial Robust and Explainable Malware Detection System for Android based Smartphones: PhD Forum Abstract.. IPSN, page 412-413. ACM, (2021)Towards Robust Android Malware Detection Models using Adversarial Learning., and . PerCom Workshops, page 424-425. IEEE, (2021)Credit Card Fraud Detection Using K-Means Combined with Supervised Learning., , , , and . HIS, volume 420 of Lecture Notes in Networks and Systems, page 262-272. Springer, (2021)Identification of Adversarial Android Intents using Reinforcement Learning., , , and . IJCNN, page 1-8. IEEE, (2021)Android Malicious Application Classification Using Clustering., , , and . ISDA (2), volume 941 of Advances in Intelligent Systems and Computing, page 659-667. Springer, (2018)DOOM: A Novel Adversarial-DRL-Based Op-Code Level Metamorphic Malware Obfuscator for the Enhancement of IDS., , and . CoRR, (2020)