文章基本信息

标题：AN INTELLIGENT AND REAL-TIME RANSOMWARE DETECTION TOOL USING MACHINE LEARNING ALGORITHM
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作者：HIBA ZUHAIR ; ALI SELAMAT
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：23
页码：3448-3461
出版社：Journal of Theoretical and Applied
摘要：Zero-day ransomware still threaten users and enterprises survival in the cyber-space by disturbing electronic amenities, damaging information systems, and causing data and money losses. The publically used anti-ransomware software are trying to mitigate this security issue, however they are limited at identifying zero-day ransomware variants effectively in the real-time without performance overhead. Thus, this paper proposed intelligent, real-time, and three-tier model of ransomware detection tool to be performed well for protecting windows-based information systems. The proposed ransomware detection tool comprises a hybrid machine learning algorithm which hybridizes the decisive functions of two topmost machine learning algorithms (Na�ve Bays and Decision Tree) to holistically characterize and accurately classify zero-day ransomware variants in real-time application. Empirical, comparative and realistic assessments demonstrate the adaptability and effectiveness of the proposed ransomware detection tool versus zero-day ransomwares. It achieves approximate accuracy rate of (96. 27%) and mistake rate of (1.32%) along with low misclassifications throughout real-time practice.
关键词：Zero-day ransomwares; Signature-based detection; Anomaly-based detection; Hybrid-based detection; Dynamic traits; Hybrid machine learning algorithms.