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A secure model against mobile sink replication attacks in unattended sensor networks
Zhou, Boqing Li, Sujun Wang, Jianxin Cheng, Yun
Zhou, Boqing
Li, Sujun
Wang, Jianxin
Cheng, Yun
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Journal article
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2022-12-24
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Computer and Information Sciences
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https://doi.org/10.1016/j.comnet.2022.109529
Abstract
Unattended wireless sensor networks (UWSNs), in which mobile sinks (MSs) are responsible for the data collection, and which are vulnerable to multiple attacks. For example, the adversaries can initiate MS replication attack after compromising a large number of sensor nodes (SNs). To resist such attacks, some scholars have proposed some schemes. In these schemes, it is assumed that MSs are equipped with tamper resistance hardware, so they are pre-distributed most of keys of a key pool for achieving authentication with SNs. However, in outdoor and even sensitive areas, tamper-resistant hardware is not always absolutely safe. Once MSs are compromised, networks become insecure. In this paper, we propose a secure model. The model contains three types of nodes, namely SNs, MSs, and a base station (BS). MSs are responsible for collecting the data encrypted by SNs and forwarding it to BS; BS is responsible for decrypting and analyzing the collected data. During the data collection process, an MS can collect an SN's data only after being authenticated by the SN using the pre-distribution key information. But it cannot decrypt the collected data. In this model, the adversaries can induce a new type of false data injection attack. In the attack, the replicated MSs impersonate uncompromised SNs to send false data to BS by using the compromised key information. If BS accepts a large amount of false data, it will make a wrong judgment. Analysis and simulation show that the proposed model has excellent resistance against MS replication attacks and false data injection attacks by setting appropriate parameter values.
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Boqing Zhou, Sujun Li, Jianxin Wang, Yun Cheng, Jie Wu, A secure model against mobile sink replication attacks in unattended sensor networks, Computer Networks, Volume 221, 2023, 109529, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2022.109529.
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Elsevier
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Computer Networks, Vol. 221
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