A Linguistic Analysis Metric in Detecting Ransomware Cyber-attacks

Originating and striking from anywhere, cyber-attacks have become ever more sophisticated in our modern society and users are forced to adopt increasingly good and vigilant practices to protect from them. Among these, ransomware remains a major cyber-attack whose major threat to end users (disrupted...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced computer science & applications 2021, Vol.12 (11)
Main Authors: Florea, Diana, Patterson, Wayne
Format: Article
Language:English
Subjects:
Computer science
COVID-19
Cybersecurity
End users
Extortion
Language
Language usage
Languages
Linguistics
Machine translation
Malware
Ransomware
Research methodology
Semantics
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Originating and striking from anywhere, cyber-attacks have become ever more sophisticated in our modern society and users are forced to adopt increasingly good and vigilant practices to protect from them. Among these, ransomware remains a major cyber-attack whose major threat to end users (disrupted operations, restricted files, scrambled sensitive data, financial demands, etc.) does not particularly lie in number but in severity. In this study we explore the possibility of real-time detection of ransomware source through a linguistic analysis that examines machine translation relative to the Levenshtein Distance and may thereby provide important indications as to attacker’s language of origin. Specifically, the aim of our research is to advance a metric to assist in determining whether an external ransom text is an indicator of either a human- or a machine-generated cyber-attack. Our proposed method works its argument on a set of Eastern European languages but is applicable to a large(r) range of languages and/or probabilistic patterns, being characterized by usage of limited resources and scalability properties.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2021.0121158