dc.contributor.author | Ahmed, Usman | |
dc.contributor.author | Lin, Jerry Chun-Wei | |
dc.contributor.author | Srivastava, Gautam | |
dc.date.accessioned | 2022-04-28T07:34:09Z | |
dc.date.available | 2022-04-28T07:34:09Z | |
dc.date.created | 2022-01-27T15:22:38Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Ahmed, U., Lin, J. C.-W., & Srivastava, G. (2021). Generative ensemble learning for mitigating adversarial malware detection in IOT. In 2021 IEEE 29th International Conference on Network Protocols (ICNP). | en_US |
dc.identifier.issn | 1092-1648 | |
dc.identifier.uri | https://hdl.handle.net/11250/2993107 | |
dc.description | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.description.abstract | This paper proposes a framework that can be employed to mitigate adversarial evasion attacks on Android malware classifiers. It extracts multiple discriminating feature subsets from a single Android app such that each subset has the potential to classify a huge dataset of malicious and benign Android apps independently. Moreover, it incorporates an ensemble of ML classifiers where each classifier is trained on different features subset. Finally, the ensemble model formulates a collaborative classification decision that is resilient against adversarial evasion attacks. Results showed that the designed model achieves good performance compared to the existing models. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.subject | adversarial evasion attacks | en_US |
dc.subject | ML-based ensemble analysis | en_US |
dc.subject | ransomware detection | en_US |
dc.title | Generative Ensemble Learning for Mitigating Adversarial Malware Detection in IoT | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © 2021 IEEE | en_US |
dc.source.pagenumber | 5 | en_US |
dc.source.journal | Proceedings - International Conference on Network Protocols (ICNP) | en_US |
dc.identifier.doi | 10.1109/ICNP52444.2021.9651917 | |
dc.identifier.cristin | 1991557 | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |