2026-06-10T18:07:03Zhttps://iris.cnr.it/oai/requestoai:iris.cnr.it:20.500.14243/5708812026-03-05T01:39:20Zcom_20.500.14243_46com_20.500.14243_21col_20.500.14243_47ou_ou294ou_ou239
A note on methods for explainable malware analysis
Homola M.
Anthony P.
Bečková I.
Kľuka J.
Mojžiš J.
Švec P.
Balogh S.
Cardillo F. A.
Debole F.
Straccia U.
Kenyeres M.
Giannini F.
Diligenti M.
Gori M.
Bisták T.
Trizna D.
Adams Z.
Homola, M.
Anthony, P.
Bečková, I.
Kľuka, J.
Mojžiš, J.
Švec, P.
Balogh, S.
Cardillo, F. A.
Debole, F.
Straccia, U.
Kenyeres, M.
Giannini, F.
Diligenti, M.
Gori, M.
Bisták, T.
Trizna, D.
Adams, Z.
Malware analysis, explainable AI, ontology, EMBER dataset
The inevitable rise of machine learning in malware analysis puts forward the need for human-understandable explanations of the learned results. We point out how the ontological representation of malware data provides a suitable language for the construction of such explanations. We then focus on possible methods that enable producing such explanations and we reflect on our experience with them in the context of the EMBER dataset.
2025
info:eu-repo/semantics/conferenceObject
https://hdl.handle.net/20.500.14243/570881
https://ceur-ws.org/Vol-4176/shields-3.pdf
eng
ispartofbook:Episode XI: The Sicilian Summer under the Etna
JOWO 2025 - Joint Ontology Workshops
volume:4176
numberofpages:14
serie:CEUR WORKSHOP PROCEEDINGS
info:eu-repo/semantics/openAccess
ELETTRONICO
CEUR-WP
license:Creative commons
license uri:http://creativecommons.org/licenses/by/4.0/