Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Husnain Rafiq"'
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-18 (2022)
Abstract Due to the widespread usage of Android smartphones in the present era, Android malware has become a grave security concern. The research community relies on publicly available datasets to keep pace with evolving malware. However, a plethora
Externí odkaz:
https://doaj.org/article/8f8440d06b3e437894397b5ebf5257f9
Publikováno v:
IEEE Access, Vol 9, Pp 78276-78292 (2021)
Machine learning (ML) based botnet detectors are no exception to traditional ML models when it comes to adversarial evasion attacks. The datasets used to train these models have also scarcity and imbalance issues. We propose a new technique named Bot
Externí odkaz:
https://doaj.org/article/d2491ed4033b46ccad07ff1a0daebb0e
Publikováno v:
IEEE Transactions on Industrial Informatics. 19:960-968
With advanced 5 G/6 G networks, data-driven interconnected devices will increase exponentially. As a result, the Industrial Internet of Things (IIoT) requires data secure information extraction to apply digital services, medical diagnoses and financi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::165750ca90323ad6a3712bb91a98f26b
https://doi.org/10.1109/tii.2022.3189046
https://doi.org/10.1109/tii.2022.3189046
Publikováno v:
IEEE Transactions on Artificial Intelligence. :1-13
A myriad of recent literary works has leveraged generative adversarial networks (GANs) to generate unseen evasion samples. The purpose is to annex the generated data with the original train set for adversarial training to improve the detection perfor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11b09eef3fb24e49c1ae6b813903f7db
https://doi.org/10.1109/tai.2022.3196283
https://doi.org/10.1109/tai.2022.3196283
Publikováno v:
2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::383e2c9829853080d46c4a886b85f05a
https://doi.org/10.1109/skima57145.2022.10029504
https://doi.org/10.1109/skima57145.2022.10029504
Publikováno v:
IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
Machine learning (ML) classifiers have been increasingly used in Android malware detection and countermeasures for the past decade. However, ML based solutions are vulnerable to adversarial evasion attacks. An attacker can craft a malicious sample ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be8940c7b8925e8f7656375e886a464e
https://doi.org/10.1109/infocomwkshps54753.2022.9798161
https://doi.org/10.1109/infocomwkshps54753.2022.9798161
Publikováno v:
TrustCom
In recent years the number and sophistication of Android malware have increased dramatically. A prototype framework which uses static analysis methods for classification is proposed which employs two feature sets to classify Android malware, permissi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cdaa34746734b27f6ebb69175ee8004e
https://doi.org/10.1109/trustcom50675.2020.00244
https://doi.org/10.1109/trustcom50675.2020.00244