Automated Behavior-based Malice Scoring of Ransomware Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{DBLP:conf/ssci/AbbasiAW21,

• author = "Muhammad Shabbir Abbasi and Harith Al-Sahaf and Ian Welch",
• title = "Automated Behavior-based Malice Scoring of Ransomware Using Genetic Programming",
• booktitle = "IEEE Symposium Series on Computational Intelligence, SSCI 2021",
• pages = "1--8",
• publisher = "IEEE",
• year = "2021",
• month = dec # " 5-7",
• address = "Orlando, FL, USA",
• keywords = "genetic algorithms, genetic programming Symbolic regression, ransomware, malice scoring",
• isbn13 = "978-1-7281-9049-5",
• timestamp = "Thu, 03 Feb 2022 09:28:31 +0100",
• biburl = "https://dblp.org/rec/conf/ssci/AbbasiAW21.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://doi.org/10.1109/SSCI50451.2021.9660009",
• DOI = "doi:10.1109/SSCI50451.2021.9660009",
• size = "8 pages",
• 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