IPv4互联网背景辐射数据集小子网等效采样精度量化研究

Stones Dalitso Chindipha, B. Irwin, Alan Herbert
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引用次数: 2

摘要

网络望远镜已经使用了十多年,通过收集未经请求的网络流量来帮助识别威胁。这种互联网背景辐射(IBR)数据已被证明是在整个互联网上打击新出现的威胁的重要情报来源。传统上,操作需要大量连续的IP地址块。由于全球IPv4地址短缺,研究人员继续操作这种传感器并将其作为操作智能的一部分被组织采用正成为一项挑战。出于运营目的而使用分配的IP地址的压力很大。IBR收集方法的未来使用可能仅限于较小的IP地址池,这些地址池可能不是连续的。本文为评估这种小型传感器的可行性提供了第一步。对各种子网大小的等效随机抽样进行了评估。将可观测数据的精度与使用/24网络块的传统“小型”IPv4网络望远镜进行比较。结果表明,对于大多数IBR数据,由较小的、不连续的地址块组成的传感器与基本情况相比能够达到较高的准确率。虽然获得的结果考虑到IBR的当前性质,但它证明了组织利用其网络内的免费IP地址进行IBR收集并最终生成威胁情报的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying the Accuracy of Small Subnet-Equivalent Sampling of IPv4 Internet Background Radiation Datasets
Network telescopes have been used for over a decade to aid in identifying threats by gathering unsolicited network traffic. This Internet Background Radiation (IBR) data has proved to be a significant source of intelligence in combating emerging threats on the Internet at large. Traditionally, operation has required a significant contiguous block of IP addresses. Continued operation of such sensors by researchers and adoption by organisations as part of its operation intelligence is becoming a challenge due to the global shortage of IPv4 addresses. The pressure is on to use allocated IP addresses for operational purposes. Future use of IBR collection methods is likely to be limited to smaller IP address pools, which may not be contiguous. This paper offers a first step towards evaluating the feasibility of such small sensors. An evaluation is conducted of the random sampling of various subnet sized equivalents. The accuracy of observable data is compared against a traditional 'small' IPv4 network telescope using a /24 net-block. Results show that for much of the IBR data, sensors consisting of smaller, non-contiguous blocks of addresses are able to achieve high accuracy rates vs. the base case. While the results obtained given the current nature of IBR, it proves the viability for organisations to utilise free IP addresses within their networks for IBR collection and ultimately the production of Threat intelligence.
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