A Prefix Hijacking Detection Model Based on the Immune Network Theory

A Prefix Hijacking Detection Model Based on the Immune Network Theory
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基于免疫网络理论的前缀劫持检测模型

DOI:
10.1109/access.2019.2941006
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发表时间:
2019-09
期刊:
影响因子:
3.9
通讯作者:
Bowen Zhao
Bowen Zhao
中科院分区:
计算机科学3区
文献类型:
--
作者:
Jian Zhang;Daofeng Li;Bowen Zhao

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前缀劫持问题是边界网关协议(BGP)安全研究中迫切需要解决的安全问题。为了解决BGP中的前缀劫持问题,本文基于免疫网络理论,提出了一种新的(P)重缀(H)劫持(D)模型,称为APHD。具体地说,APPD使用真实的BGP更新消息进行预训练,并具有在预训练后实时检测更新消息的能力。APHD(1)能够有效地检测到前缀劫持攻击,准确率高;(2)易于部署;(3)误报率低,开销低。广泛的性能评估表明,我们的解决方案是安全和可行的。APID的准确率提高了6.2%,假阳性率降低了85.7%。
The prefix hijacking problem is an urgent security issue that need to address in the Border Gateway Protocol (BGP) security research. In order to solve the problem of prefix hijacking in BGP, we propose (a) new (p)refix (h)ijacking (d)etection model based on the immune network theory in this paper, called aPHD. To be specific, aPHD uses real BGP UPDATE messages for pre-training and has the ability to detect UPDATE messages in real time after pre-training. The aPHD (1) can effectively detect prefix hijacking attacks with high accuracy; (2)is easy to deployment; and (3) has a low false positive rate and low overhead. Extensive performance evaluation shows that our solution is secure and feasible. The aPHD improved the accuracy rate by 6.2% and reduced the false positive rate by 85.7%.
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