Automated Behavior-based Malice Scoring of Ransomware Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{DBLP:conf/ssci/AbbasiAW21,
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author = "Muhammad Shabbir Abbasi and Harith Al-Sahaf and
Ian Welch",
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title = "Automated Behavior-based Malice Scoring of Ransomware
Using Genetic Programming",
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booktitle = "IEEE Symposium Series on Computational Intelligence,
SSCI 2021",
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pages = "1--8",
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publisher = "IEEE",
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year = "2021",
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month = dec # " 5-7",
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address = "Orlando, FL, USA",
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keywords = "genetic algorithms, genetic programming Symbolic
regression, ransomware, malice scoring",
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isbn13 = "978-1-7281-9049-5",
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timestamp = "Thu, 03 Feb 2022 09:28:31 +0100",
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biburl = "https://dblp.org/rec/conf/ssci/AbbasiAW21.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "https://doi.org/10.1109/SSCI50451.2021.9660009",
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DOI = "doi:10.1109/SSCI50451.2021.9660009",
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size = "8 pages",
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abstract = "Malice or severity scoring models are a technique for
detection of maliciousness. A few ransom-ware detection
studies use malice scoring models for detection of
ransomware-like behaviour. These models rely on the
weighted sum of some manually chosen features and their
weights by a domain expert. To automate the modelling
of malice scoring for ransomware detection, we propose
a method based on Genetic Programming (GP) that
automatically evolves a behavior-based malice scoring
model by selecting appropriate features and functions
from the input feature and operator sets. The
experimental results show that the best-evolved model
correctly assigned a malice score, below the threshold
value to over 85percent of the unseen goodware
instances, and over the threshold value to more than
99percent of the unseen ransomware instances.",
- }
Genetic Programming entries for
Muhammad Shabbir Abbasi
Harith Al-Sahaf
Ian Welch
Citations