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首页> 外文会议>Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining >Argumentation models for cyber attribution
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Argumentation models for cyber attribution

机译：网络归因的论证模型

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摘要

A major challenge in cyber-threat analysis is combining information from different sources to find the person or the group responsible for the cyber-attack. It is one of the most important technical and policy challenges in cybersecurity. The lack of ground truth for an individual responsible for an attack has limited previous studies. In this paper, we take a first step towards overcoming this limitation by building a dataset from the capture-the-flag event held at DEFCON, and propose an argumentation model based on a formal reasoning framework called DeLP (Defeasible Logic Programming) designed to aid an analyst in attributing a cyber-attack. We build models from latent variables to reduce the search space of culprits (attackers), and show that this reduction significantly improves the performance of classification-based approaches from 37% to 62% in identifying the attacker.
机译：网络威胁分析的一个主要挑战是组合来自不同来源的信息以找到负责网络攻击的人员或组织。这是网络安全中最重要的技术和政策挑战之一。负责进攻的个人缺乏事实依据，限制了先前的研究。在本文中，我们通过克服DEFCON举行的flag-the-flag事件构建数据集，迈出了克服这一局限的第一步，并提出了一种基于正式推理框架DeLP（Defeasible Logic Programming）的论证模型，旨在帮助分析人员对网络攻击的归因。我们从潜在变量构建模型以减少罪犯（攻击者）的搜索空间，并表明这种减少将识别攻击者的分类方法的性能从37％显着提高到62％。

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