预测网络安全事件的方法

Yaser M.A. Abualkas, D. Bhaskari
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引用次数: 0

摘要

数据科学可用于检测、预防和解决不断变化的网络安全风险。CSDS是一个快速发展的领域。在网络安全方面,CSDS强调数据的使用,专注于生成针对特定威胁的警告,并在试图增强网络安全运营的过程中使用推理方法对用户行为进行分类。数据科学是网络安全技术和运营最新发展的核心。只有通过从网络安全数据中提取模式和见解,以及创建反映这些模式和见解的数据驱动模型,安全系统中的自动化和智能才有可能实现。本工作试图描述以安全为重点的各种数据驱动研究方法。按照技术的各个阶段,每一项预测网络事件的工作都被彻底调查,以创建一个自动化和智能的安全系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methodologies for Predicting Cybersecurity Incidents
Data science may be used to detect, prevent, and address ever-evolving cybersecurity risks. CSDS is a fast developing field. When it comes to cybersecurity, CSDS emphasises the use of data, concentrates on generating warnings that are specific to a particular threat and uses inferential methods to categorise user behaviour in the process of attempting to enhance cybersecurity operations. Data science is at the heart of recent developments in cybersecurity technology and operations. Automation and intelligence in security systems are only possible through the extraction of patterns and insights from cybersecurity data, as well as the creation of data-driven models that reflect those patterns and insights An attempt is made in this work to describe the various data-driven research approaches with a focus on security. In accordance with the phases of the technique, each work that anticipates cyber-incidents is thoroughly investigated to create an automated and intelligent security system.
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