基于Spark的统一主机入侵检测框架

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI:10.1109/TrustCom50675.2020.00026

Ming Liu, Zhi Xue, Xiangjian He

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引用次数: 2

摘要

基于主机的入侵检测系统(HIDS)是网络安全研究的一个重要领域。HIDS通过检查主机的日志数据来识别入侵行为。检测效率是影响HIDS的重要因素。传统上，HIDS通常以独立模式安装。在计算资源有限的单个物理计算机上训练具有大量数据的检测引擎可能非常耗时。因此，本文提出了一个基于Spark并部署在Google云上的统一HIDS框架。该框架包括一个统一的机器学习管道，以实现可扩展和高效的HIDS。

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 Unified Host-based Intrusion Detection Framework using Spark in Cloud

The host-based intrusion detection system (HIDS) is an essential research domain of cybersecurity. HIDS examines log data of hosts to identify intrusive behaviors. The detection efficiency is a significant factor of HIDS. Traditionally, HIDS is often installed with a standalone mode. Training detection engines with a large amount of data on a single physical computer with limited computing resources may be time-consuming. Therefore, this paper offers a unified HIDS framework based on Spark and deployed in the Google cloud. The framework includes a unified machine learning pipeline to implement scalable and efficient HIDS.

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来源期刊

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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