Zobrazeno 1 - 10 of 50 pro vyhledávání: '"Dima Alhadidi"'
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Akademický článek
Comparative Analysis of Membership Inference Attacks in Federated and Centralized Learning
Autor: Ali Abbasi Tadi, Saroj Dayal, Dima Alhadidi, Noman Mohammed
Publikováno v: Information, Vol 14, Iss 11, p 620 (2023)
The vulnerability of machine learning models to membership inference attacks, which aim to determine whether a specific record belongs to the training dataset, is explored in this paper. Federated learning allows multiple parties to independently tra
Externí odkaz: https://doaj.org/article/24462afc440c4f3f8826a34fa87de7b3
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2
Akademický článek
Secure approximation of edit distance on genomic data
Autor: Md Momin Al Aziz, Dima Alhadidi, Noman Mohammed
Publikováno v: BMC Medical Genomics, Vol 10, Iss S2, Pp 55-67 (2017)
Abstract Background Edit distance is a well established metric to quantify how dissimilar two strings are by counting the minimum number of operations required to transform one string into the other. It is utilized in the domain of human genomic sequ
Externí odkaz: https://doaj.org/article/f51173ba57c64b8798e9943d1e26545d
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8
Effective and Efficient Hybrid Android Malware Classification Using Pseudo-Label Stacked Auto-Encoder
Autor: Samaneh Mahdavifar, Ali A. Ghorbani, Dima Alhadidi
Publikováno v: Journal of Network and Systems Management. 30
Android has become the target of attackers because of its popularity. The detection of Android mobile malware has become increasingly important due to its significant threat. Supervised machine learning, which has been used to detect Android malware
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ef66ad7d2f9a5ca2aeccf5ad2b38ded0
https://doi.org/10.1007/s10922-021-09634-4
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9
Membership Inference Attacks: Analysis and Mitigation
Autor: Shamimur Rahman Shuvo, Dima Alhadidi
Publikováno v: TrustCom
Given a machine learning model and a record, membership attacks determine whether this record was used as part of the model's training dataset. Membership inference can present a risk to private datasets if these datasets are used to train machine le
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::67044b39335796e3926a5f457588c4b9
https://doi.org/10.1109/trustcom50675.2020.00190
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10
Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning
Autor: Ali A. Ghorbani, Dima Alhadidi, Rasool Fatemi, Samaneh Mahdavifar, Andi Fitriah Abdul Kadir
Publikováno v: DASC/PiCom/CBDCom/CyberSciTech
Due to the significant threat of Android mobile malware, its detection has become increasingly important. Despite the academic and industrial attempts, devising a robust and efficient solution for Android malware detection and category classification
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c058782b17b5809374f591d31aaa6bda
https://doi.org/10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00094
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