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A Mechanism for Mitigating DoS attack in Information-Centric Networks
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
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2017 (English)In: Proceedings of the 1st International Conference on Internet of Things and Machine Learning, Association for Computing Machinery (ACM), 2017, article id 26Conference paper, Published paper (Refereed)
Abstract [en]

Information-Centric Networking (ICN) 1 is a significant networking paradigm for the Internet of Things, which is an information-centric network in essence. The ICN paradigm owns inherently some security features, but also brings several new vulnerabilities. The most significant one among them is Interest flooding, which is a new type of Denial of Service (DoS) attack, and has even more serious effects to the whole network in the ICN paradigm than in the traditional IP paradigm. In this paper, we suggest a new mechanism to mitigate Interest flooding attack. The detection of Interest flooding and the corresponding mitigation measures are implemented on the edge routers, which are directly connected with the attackers. By using statistics of Interest satisfaction rate on the incoming interface of some edge routers, malicious name-prefixes or interfaces can be discovered, and then dropped or slowed down accordingly. With the help of the network information, the detected malicious name-prefixes and interfaces can also be distributed to the whole network quickly, and the attack can be mitigated quickly. The simulation results show that the suggested mechanism can reduce the influence of the Interest flooding quickly, and the network performance can recover automatically to the normal state without hurting the legitimate users.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2017. article id 26
Keywords [en]
Information Centric Networking, Security, Denial-of-Service, Interest Flooding Attack
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-158965DOI: 10.1145/3109761.3109787ISI: 000463548100026ISBN: 978-1-4503-4774-7 (electronic)OAI: oai:DiVA.org:su-158965DiVA, id: diva2:1240079
Conference
1st International Conference on Internet of Things and Machine Learning, Liverpool, United Kingdom, October 17 - 18, 2017
Available from: 2018-08-20 Created: 2018-08-20 Last updated: 2022-02-26Bibliographically approved

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Li, YuhongRahmani, RahimKanter, Theo

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