Alert Fatigue in Security Operations Centres: Research Challenges and Opportunities

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Shahroz Tariq, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris
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引用次数: 0

Abstract

A security operations centre (SOC) is a facility where teams of security professionals, supported by advanced technologies and processes, work together to monitor, detect, and respond to cybersecurity incidents. With advances in AI technology, most of the SOC functions are increasingly becoming AI-driven. Among these, real-time alert monitoring and triage is particularly important. Recent studies, by both industry and academia, have highlighted the problem of alert fatigue and burnout in SOC. Several solutions have been proposed in the literature and by the industry to address this problem. In this paper, we review the existing literature and industry solutions on alert fatigue mitigation through the lenses of automation, augmentation, and human-AI collaboration. Based on the review, we identify four major causes of alert fatigue in SOC. We also examine the shortcomings of existing solutions and propose several potential research directions leveraging AI. By providing a comprehensive analysis of the state-of-the-art approaches and their limitations, this study contributes to the existing literature in an important field of study. We anticipate that it will inspire new research directions for addressing alert fatigue not just in SOCs but across other Command and Control (C2) domains as well.
安全运营中心的警报疲劳:研究挑战与机遇
安全运营中心(SOC)是由安全专业人员组成的团队在先进技术和流程的支持下共同监控、检测和响应网络安全事件的设施。随着人工智能技术的进步,大多数SOC功能越来越多地由人工智能驱动。其中,实时警报监测和分诊尤为重要。近年来,业界和学术界的研究都强调了SOC中的警觉疲劳和倦怠问题。为了解决这个问题,文献和业界已经提出了几种解决方案。在本文中,我们回顾了现有的文献和行业解决方案,通过自动化、增强和人类-人工智能协作的视角来缓解警报疲劳。在此基础上,我们确定了SOC中警觉性疲劳的四个主要原因。我们还研究了现有解决方案的缺点,并提出了利用人工智能的几个潜在研究方向。通过对最新研究方法及其局限性的综合分析,本研究对这一重要研究领域的现有文献做出了贡献。我们预计,它将激发新的研究方向,不仅在soc,而且在其他指挥与控制(C2)领域解决警报疲劳问题。
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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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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