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Updated: Jul 23, 2025

Automated Detection and Analysis of Exocytosis

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

SELID:用于入侵检测数据集的选择性事件标记.

Woohyuk Jang¹, Hyunmin Kim¹, Hyungbin Seo¹

¹Department of Computer Science, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea.

Sensors (Basel, Switzerland)

|July 14, 2023

概括

安全运营中心面临着由于事件数量过大而造成的警报疲劳. 这项研究使用无监督集群引入了高效的数据标签,将标签需求减少到仅2%而不会影响机器学习模型的性能.

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科学领域:

网络安全网络安全
机器学习机器学习
数据科学数据科学数据科学

背景情况:

安全操作中心 (SOC) 被大量的安全事件所压倒,导致人类分析师的警报疲劳.
机器学习 (ML) 通过自动化区分真假安全警报提供了一个解决方案.
有效的ML模型培训需要准确标记数据集,但手动标记是资源密集的.

研究的目的:

提出一种新的选择性抽样方案,以在网络安全领域有效地进行数据标签.
为了减少标记大型数据集所需的人力资源,用于基于机器学习的安全事件分析.

主要方法:

将安全事件字节序列转换为固定大小的矢量,使用内容定义的块和特征散列.
将无监督集群算法应用于这些向量.
从每个集群中选择一小部分样本进行手动标签.

主要成果:

拟议的选择性抽样方案大大减少了数据标签的努力,只需要2%的总数据.
实验结果显示,尽管标签减少,机器学习模型的F1分数没有降低.
在私人SOC数据集和公共互联网数据集上进行验证.

结论:

开发的选择性抽样方案为网络安全中的数据标签提供了一种有效的方法.
这种方法有效地减轻了警报疲劳,通过实现更快,更准确的ML模型训练.
该技术经过验证,可在现实世界安全操作中实际应用.

关键词:

警报疲劳疲劳的情况网络安全网络安全检测入侵检测入侵安全运营中心安全运营中心没有监督的学习学习.

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