MODELS AND METHODS FOR DIAGNOSING ZERO-DAY THREATS IN CYBERSPACE

Oleksandr S. Saprykin
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Abstract

The article is devoted to the development of models and methods for detecting Zero-Day threats in cyberspace to improve the efficiency of detecting high-level malicious complexes that are using polymorphic mutators. The method for detecting samples by antivirus solutions using a public and local multiscanner is proposed. The method for diagnosing polymorphic malware using Yara rules is being developed. The multicomponent service that allows organizing a free malware analysis solution with a hybrid deployment architecture in public and private clouds is described. The cloud service for detecting malware based on open-source sandboxes and MAS, allowing horizontal scalability in hybrid clouds, and showing high capacity during malicious and non-malicious object processing is designed. The main task of the service is to collect artifacts after dynamic and static object analysis to detect zero-day threats. The effectiveness of the proposed solutions is shown. Scientific novelty and originality consist in the creation of the following methods: 1) detecting the sample by preinstalled antivirus solutions that allow static scanning in separate threads without requests restrictions for increasing the malware processing speed and restrict public access to confidential files; 2) diagnosing polymorphic malware using Yara rules, that allows detecting new modifications that are not detected by available solutions. The proposed hybrid system architecture allows to perform a retrospective search by families, tracking changes in destructive components, collect the malicious URLs database to block traffic to C&C servers, collect dropped and downloaded files, analyze phishing emails attachments, integrate with SIEM, IDS, IPS, antiphishing and Honeypot systems, improve the quality of the SOC analyst, decrease the incidents response times and block new threats that are not detected by available antivirus solutions. The practical significance of the results is in the cloud service development that combines MAS Sandbox and a modified distributed Cuckoo sandbox, which allows to respond to Zero-Day threats quickly, store a knowledge base for artifacts correlation between polymorphic malware samples, actively search for new malware samples and integrate with cyber protection hardware and software systems that support the Cuckoo API.
网络空间零日威胁诊断模型与方法
本文致力于开发用于检测网络空间零日威胁的模型和方法,以提高检测使用多态突变子的高级恶意复合物的效率。提出了一种利用公共和本地多重扫描器对病毒样本进行检测的方法。利用Yara规则诊断多态恶意软件的方法正在开发中。描述了允许在公共云和私有云中使用混合部署架构组织免费恶意软件分析解决方案的多组件服务。设计了基于开源沙箱和MAS的恶意软件检测云服务,允许在混合云中横向扩展,在恶意和非恶意对象处理过程中显示高容量。该服务的主要任务是收集动态和静态对象分析后的工件,以检测零日威胁。所提出的解决方案是有效的。科学的新颖性和独创性在于创造了以下方法:1)通过预先安装的防病毒解决方案检测样本,允许在单独的线程中进行静态扫描,不受请求限制,提高恶意软件处理速度,限制公众访问机密文件;2)使用Yara规则诊断多态恶意软件,可以检测到现有解决方案无法检测到的新修改。提出的混合系统架构允许按家族执行回顾性搜索,跟踪破坏性组件的变化,收集恶意url数据库以阻止流量到C&C服务器,收集丢失和下载的文件,分析网络钓鱼电子邮件附件,集成SIEM, IDS, IPS,反网络钓鱼和Honeypot系统,提高SOC分析师的质量。减少事件响应时间并阻止可用的防病毒解决方案未检测到的新威胁。研究结果的实际意义在于,结合MAS沙盒和改进的分布式杜鹃沙盒的云服务开发,可以快速响应零日威胁,存储多态恶意软件样本之间工件相关性的知识库,主动搜索新的恶意软件样本,并与支持杜鹃API的网络保护硬件和软件系统集成。
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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