LTrust: An Adaptive Trust Model Based on LSTM for Underwater Acoustic Sensor Networks
LTrust: An Adaptive Trust Model Based on LSTM for Underwater Acoustic Sensor Networks
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LTrust：基于 LSTM 的水声传感器网络自适应信任模型
DOI：
10.1109/twc.2022.3157621
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发表时间：
2022
影响因子：
10.4
通讯作者：
Miguel Martinez-Garcia
中科院分区：
文献类型：
--
作者：
Jiaxin Du;Guangjie Han;Chuan Lin;Miguel Martinez-Garcia
作为一种有效的安全机制，信任模型被提出用于估计水声传感器网络中单个节点在恶意攻击时的可靠性。然而，现有的信任模型忽略了网络拓扑中不同节点的相对重要性。此外，很少有信任模型研究缺陷推荐信任过滤的效果。在这项工作中，我们提出了一种基于长短期记忆（LSTM）网络模型的自适应信任模型，我们称之为LTrust。LTrust由信任数据收集和信任评估两个阶段组成。在第一阶段，通过聚合通信信任和环境信任指标，利用网络拓扑的特征来评估直接信任证据；为了在节点间广播准确的信任推荐，设计了一种缺陷推荐过滤方法。第二阶段，基于LSTM模型设计自适应信任模型，通过评估异常节点的信任值来识别异常节点。LTrust模型已经在混合攻击和单模攻击场景下进行了测试。仿真结果表明，与文献中提出的其他方法相比，LTrust在信任值、准确率和错误率方面都取得了有效的性能。
As an effective security mechanism, trust models have been proposed to estimate the reliability of the individual nodes in Underwater Acoustic Sensor Networks (UASNs) during adverse attacks. However, existing trust models neglect the relative importance of the different nodes within the network topology. Further, few trust models study the effects of defective recommendation trust filtering. In this work, we propose an adaptive trust model based on the Long Short-Term Memory (LSTM) network model for UASNs, which we term LTrust. The LTrust is composed of two stages: trust data collection and trust evaluation. In the first stage, the characteristics of the network topology are leveraged towards evaluating direct trust evidence, by aggregating the communication trust and environment trust metrics; a defective recommendation filtering method is designed for broadcasting accurate trust recommendations among the nodes. In the second stage, an adaptive trust model is designed based on the LSTM model, to identify anomalous nodes by evaluating their trust value. The LTrust model has been tested under both hybrid attack and single-mode attack scenarios. Simulation results demonstrate that the LTrust achieves effective performance, as compared to other approaches proposed in the literature, in terms of trust value, accuracy and error rate.
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影响因子：
10.4
作者：
Guangjie Han;Jiaxin Du;Chuan Lin;Hongyi Wu;M. Guizani
通讯作者：
Guangjie Han;Jiaxin Du;Chuan Lin;Hongyi Wu;M. Guizani
影响因子：
2.2
作者：
N. Goyal;M. Dave;A. Verma
通讯作者：
N. Goyal;M. Dave;A. Verma
DOI：
10.1016/j.future.2018.05.086
发表时间：
2018-11
期刊：
Future Gener. Comput. Syst.
影响因子：
--
作者：
Gabriel G. Castañé;Huanhuan Xiong;Dapeng Dong;J. Morrison
通讯作者：
Gabriel G. Castañé;Huanhuan Xiong;Dapeng Dong;J. Morrison
影响因子：
9.3
作者：
Yuhang Wang;Zhihong Tian;Yanbin Sun;Xiaojiang Du;Nadra Guizani
通讯作者：
Yuhang Wang;Zhihong Tian;Yanbin Sun;Xiaojiang Du;Nadra Guizani
影响因子：
7.9
作者：
Du, Jiaxin;Han, Guangjie;Martinez-Garcia, Miguel
通讯作者：
Martinez-Garcia, Miguel