SCAN:命名数据网络中的智能协同攻击

Madhurima Buragohain, Chinmoy Jyoti Kathar, Chinmoy Kachari, S. Nandi, Sukumar Nandi
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引用次数: 0

摘要

命名数据网络是一种很有前途的未来互联网架构,消费者使用唯一名称从网络获取内容。它具有内置的安全特性,如数据完整性和数据源身份验证,因为内容由原始生产者签名。尽管NDN的状态转发平面有助于消除当前IP架构中存在的一些DDoS攻击,但它仍然可能面临一种称为兴趣泛洪攻击(IFA)的特定类型的攻击。在本文中,我们重点研究了IFA的一种变体,其中恶意消费者和恶意生产者协同发起攻击以降低合法消费者的QoS。在这里,我们展示了攻击者如何智能地修改利息发送速率,从而使攻击不容易被发现。仿真结果表明,通过合理配置恶意消费者和恶意生产者,攻击者可以在流量适中、恶意消费者数量较少的情况下成功发起攻击。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SCAN: Smart Collaborative Attack in Named Data Networking
Named Data Networking is a promising Future Internet Architecture, where consumers fetch contents from the network using unique names. It has in-built security features like data integrity, and data origin authentication as contents are signed by the original producer. Though, NDN’s stateful forwarding plane helps to remove some of the existing DDoS attacks in the current IP architecture, still it can face one specific kind of attack called Interest flooding attack (IFA). In this paper, we focus on one variant of IFA where malicious consumers and malicious producers collaboratively launch an attack to degrade the QoS of legitimate consumers. Here we have shown how attackers can intelligently fix the Interest sending rate so that attack goes undetected easily. Simulation results demonstrate that with proper placement of malicious consumers and producers, the attackers can successfully launch the attack with moderate traffic and less number of malicious consumers.
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