A Reputation Value-Based Early Detection Mechanism against the Consumer-Provider Collusive Attack in Information-Centric IoT

Ting Zhi, Ying Liu*, Jun Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

As the Internet of Things (IoT) has connected large number of devices to the Internet, it is urgently needed to guarantee the low latency, security, scalable content distribution of the IoT network. The benefits of Information-Centric Networking (ICN) in terms of fast and efficient data delivery and improved reliability have raised ICN as a highly promising networking model for IoT environments. However, with the widely spread of the viruses and the explosion of kinds of network devices, the attackers can easily control the devices to form a botnet such as the Mirai. Once the devices are under control, the attackers can launch a consumer-provider collusive attack in the Information-Centric IoT context. In this attack, the malicious clients issue Interest packets that can only be satisfied by the malicious content provider, and the malicious provider replies to the clients just before exceeding the Pending Interest Table entry's expiration time, to occupy the limited resources. In this paper, we expound the model of the consumer-provider collusive attack and analyze the negative effect of the attack. Then we propose a Reputation Value based Early Detection (RVED) mechanism to relieve the impact of the collusive attack. The method aims to adjust the packet dropping rates of different interfaces based on their reputation value, thus to protect the legitimate packets from being dropped as possible. We implement the consumer-provider collusive model and evaluate our defend mechanism in the simulator, and simulation results verify the feasibility and effectiveness against the collusive attack of the RVED mechanism.

Original languageEnglish
Article number9007728
Pages (from-to)38262-38275
Number of pages14
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • collusive attack
  • early detection
  • Information-centric networking
  • Internet of Things
  • reputation value

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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