Beyond gut instincts: Understanding, rating and comparing self-learning IDSs

Markus Wurzenberger, Florian Skopik, Giuseppe Settanni, Roman Fiedler
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引用次数: 1

Abstract

Today ICT networks are the economy's vital backbone. While their complexity continuously evolves, sophisticated and targeted cyber attacks such as Advanced Persistent Threats (APTs) become increasingly fatal for organizations. Numerous highly developed Intrusion Detection Systems (IDSs) promise to detect certain characteristics of APTs, but no mechanism which allows to rate, compare and evaluate them with respect to specific customer infrastructures is currently available. In this paper, we present BAESE, a system which enables vendor independent and objective rating and comparison of IDSs based on small sets of customer network data.
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超越直觉:理解、评价和比较自学的ids
今天,信息通信技术网络是经济的重要支柱。随着其复杂性的不断发展,高级持续性威胁(apt)等复杂且有针对性的网络攻击对组织来说变得越来越致命。许多高度发达的入侵检测系统(ids)承诺检测apt的某些特征,但目前还没有机制可以根据特定的客户基础设施对它们进行评级、比较和评估。在本文中,我们提出了BAESE,一个系统,使供应商独立和客观的评级和比较的ids基于小集的客户网络数据。
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