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A Generic Secure Transmission Scheme Based on Random Linear Network Coding

delete2022-04-01
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PRE
AI
R
Renyong Wu *
M
Ma, Jieming
Z
Zhixiang Tang
K
Kim‐Kwang Raymond Choo
DOI:10.1109/TNET.2021.3124890delete
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摘要

摘要

En 中文
Unlike general routing strategies, network coding (NC) can combine encoding functions with multi-path propagation over a network. This allows network capacity to be achieved to support complex security solutions. Moreover, NC has intrinsic security advantages against passive attacks over traditional routing techniques. However, due to the transmission of the global encoding kernels, the system is fragile to eavesdropping attacks with multiple probes. This paper proposes a generic unicast secure transmission scheme based on random linear network coding (RLNC). Specifically, the intended receiver generates a random matrix upon receiving the request from the source node, and then transmits each row vector of this matrix over a link reversely to the source node. Each intermediate node rearranges all received vectors to form a matrix by row, and then post-multiplies its local encoding kernel by this matrix to obtain a new matrix. Similarly, each row vector of the new matrix is reversely transmitted over a link to the source node. This procedure is performed until we have the source node, where the generalized inverse of the received matrix (or part of it) can be used as its local encoding kernel. Hence, the intended receiver can use the generated matrix (or the corresponding part) to decode the received data packets directly. We also analyze the security to demonstrate that the proposed scheme is at least as secure as other methods against wiretapping attacks. We also evaluate the performance of the proposed scheme to demonstrate its utility.
Keyword:
Encoding
Kernel
Network coding
Receivers
Codes
Routing
Unicast
Secure network coding
multi-path propagation
eavesdropping attack
random linear network coding

期刊

I
IEEE-ACM Transactions on Networking
IF:
3.6
论文数:
4.4K
被引数:
9.5K

机构

H
Hunan University
学者数:
3.6K
论文数: 1.1K
被引数: 0
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