攻击基于相似度的符号预测

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI:10.1109/ICDM51629.2021.00173

M. T. Godziszewski, Tomasz P. Michalak, Marcin Waniek, Talal Rahwan, Kai Zhou, Yulin Zhu

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引用次数: 7

摘要

在本文中，我们提出了攻击符号预测问题的计算分析，其中攻击者(网络成员)的目的是通过删除一些其他非目标链接的符号来向防御者(分析者)隐藏目标链接集的符号。如果使用局部或全局相似性度量来进行符号预测，问题就会变成np困难。我们提出了一种启发式算法，并在几个真实数据集和合成数据集上测试了它的有效性。

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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Attacking Similarity-Based Sign Prediction

In this paper, we present a computational analysis of the problem of attacking sign prediction, whereby the aim of the attacker (a network member) is to hide from the defender (an analyst) the signs of a target set of links by removing the signs of some other, non-target, links. The problem turns out to be NP-hard if either local or global similarity measures are used for sign prediction. We propose a heuristic algorithm and test its effectiveness on several real-life and synthetic datasets.

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2021 IEEE International Conference on Data Mining (ICDM)

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