VoterChoice: A ransomware detection honeypot with multiple voting framework. (6th April 2020)

• Record Type: Journal Article
• Authors: Keong Ng, Chee; Rajasegarar, Sutharshan; Pan, Lei; Jiang, Frank; Zhang, Leo Yu
• Is Part Of: Concurrency and computation. Volume 32:Number 14(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-06
• Subjects: dynamic analysis -- machine learning -- malware -- ransomware -- static analysis; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5726
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: This research presents a novel framework comprising the IPS gateway, analysis system, and honeypot for identifying and detecting ransomware based on the client honeypot concept, and active interception of downloads using Suricata inline intruder prevention system. Unlike previous frameworks that report on the accuracy rate of detecting ransomware, the proposed framework features a multiple voting platform for the validation of confidence levels in the accuracy detection rates. The proposed framework achieves high accuracy levels than other machine learning models for the detection of ransomware.