API-Based Ransomware Detection Using Machine Learning-Based Threat Detection Models

May Almousa, Sai Basavaraju, Mohd Anwar
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引用次数: 8

Abstract

Ransomware is a major malware attack experienced by large corporations and healthcare services. Ransomware employs the idea of cryptovirology, which uses cryptography to design malware. The goal of ransomware is to extort ransom by threatening the victim with the destruction of their data. Ransomware typically involves a 3-step process: analyzing the victim’s network traffic, identifying a vulnerability, and then exploiting it. Thus, the detection of ransomware has become an important undertaking that involves various sophisticated solutions for improving security. To further enhance ransomware detection capabilities, this paper focuses on an Application Programming Interface (API)-based ransomware detection approach in combination with machine learning (ML) techniques. The focus of this research is (i) understanding the life cycle of ransomware on the Windows platform, (ii) dynamic analysis of ransomware samples to extract various features of malicious code patterns, and (iii) developing and validating machine learning-based ransomware detection models on different ransomware and benign samples. Data were collected from publicly available repositories and subjected to sandbox analysis for sampling. The sampled datasets were applied to build machine learning models. The grid search hyperparameter optimization algorithm was employed to obtain the best fit model; the results were cross-validated with the testing datasets. This analysis yielded a high ransomware detection accuracy of 99.18% for Windows-based platforms and shows the potential for achieving high-accuracy ransomware detection capabilities when using a combination of API calls and an ML model. This approach can be further utilized with existing multilayer security solutions to protect critical data from ransomware attacks.
基于api的勒索软件检测:基于机器学习的威胁检测模型
勒索软件是大型企业和医疗保健服务机构遭受的主要恶意软件攻击。勒索软件采用了密码病毒学的思想,使用密码学来设计恶意软件。勒索软件的目的是通过威胁摧毁受害者的数据来勒索赎金。勒索软件通常包括三个步骤:分析受害者的网络流量,识别漏洞,然后利用它。因此,检测勒索软件已成为一项重要的工作,涉及各种复杂的解决方案,以提高安全性。为了进一步增强勒索软件检测能力,本文重点研究了一种基于应用程序编程接口(API)的勒索软件检测方法,并结合了机器学习(ML)技术。本研究的重点是(i)了解Windows平台上勒索软件的生命周期,(ii)动态分析勒索软件样本以提取恶意代码模式的各种特征,以及(iii)在不同的勒索软件和良性样本上开发和验证基于机器学习的勒索软件检测模型。数据从公开可用的存储库中收集,并进行沙盒分析以进行抽样。将采样数据集用于构建机器学习模型。采用网格搜索超参数优化算法获得最优拟合模型;结果与测试数据集交叉验证。该分析在基于windows的平台上产生了99.18%的勒索软件检测准确率,并显示了当使用API调用和ML模型的组合时实现高精度勒索软件检测功能的潜力。这种方法可以进一步与现有的多层安全解决方案一起使用,以保护关键数据免受勒索软件攻击。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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