组织网络威胁情报模型

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2023-08-03 DOI:10.1109/icABCD59051.2023.10220503

Z. C. Khan, Thulile Mkhwanazi, M. Masango

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引用次数: 0

摘要

随着南非的网络攻击日益增多，企业需要加强网络安全控制。网络威胁情报是网络安全计划的重要组成部分，但往往被忽视。它可以帮助识别未来和潜在的网络威胁。组织处理大量包含网络威胁情报的数据，但这些数据通常不会被收集、处理或视为网络威胁情报。如果不采取行动，南非组织将继续感受到网络攻击的影响。为了使南非组织能够更清晰地利用网络威胁情报，本工作旨在为组织对网络威胁情报进行分类。讨论了网络威胁情报的几个特点，提出了网络威胁情报模型。这个模型的适用性通过一个简短的用例来证明。

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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A Model for Cyber Threat Intelligence for Organisations

As cyber attacks are increasing in South Africa, organisations need to strengthen cyber security controls. Cyber Threat Intelligence is an essential component of a Cybersecurity program but is often overlooked. It can assist to identify future and potential cyber threats. Organisations process large volumes of data containing Cyber Threat Intelligence, but this is often not collected, processed, or considered as Cyber Threat Intelligence. South African organizations will continue to feel the repercussions of cyber-attacks if actions are not taken. To bring clarity and allow South African organizations to leverage on Cyber Threat Intelligence, this work aims to categorize Cyber Threat Intelligence for organizations. Several characteristics of Cyber Threat Intelligence are discussed, and thereafter a model is presented. The applicability of this model is demonstrated by a short use-case.

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来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.