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Designing Robust Deep Learning Classifiers for Image-based Malware Analysis

Conference Paper
Publication Date:
2022
abstract:
Deep Learning models demonstrated high accuracies performance in malware classification, but they are still lacking "explainability"to ensure robustness and reliability in the generated prediction. In this short contribution, we summarize the researches that we conducted in the latest years in the Malware Analysis field.
Iris type:
04.01 Contributo in Atti di convegno
Keywords:
component; formatting; insert; style; styling
List of contributors:
Martinelli, Fabio
Authors of the University:
MARTINELLI FABIO
Handle:
https://iris.cnr.it/handle/20.500.14243/418267
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URL

http://www.scopus.com/record/display.url?eid=2-s2.0-85140881001&origin=inward
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