Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles
Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles
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DOI：
10.1109/jiot.2021.3071362
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发表时间：
2021-10
影响因子：
10.6
通讯作者：
Harsh Grover;Tejasvi Alladi;V. Chamola;Dheerendra Singh;K. Choo
中科院分区：
文献类型：
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作者：
Harsh Grover;Tejasvi Alladi;V. Chamola;Dheerendra Singh;K. Choo
车联网（IoVs）正在迅速成为我们社会的常态，但这种趋势也带来了一系列挑战（例如，由于攻击媒介的扩大，新的安全和隐私风险）。在这项工作中，我们提出了一个基于边缘计算的安全、高效和智能的多层异构物联网网络。我们首先讨论这种体系结构的功能和目标。然后，我们展示了无监督深度学习技术如何促进可疑车辆行为的识别并确保这种架构的安全性。我们的研究结果证明了堆叠长短期记忆（LSTM）模型在学习时空信息和参数效率方面优于单个LSTM模型。
Internet of Vehicles (IoVs) are fast becoming the norm in our society, but such a trend also comes with its own set of challenges (e.g., new security and privacy risks due to the expanded attack vectors). In this work, we propose an edge-computing-based secure, efficient, and intelligent multitier heterogeneous IoVs network. We first discuss the functionality and objectives of such an architecture. Then, we demonstrate how unsupervised deep learning techniques can facilitate the identification of suspicious vehicle behavior and ensure the security of such an architecture. The findings from our evaluations demonstrate the learning spatiotemporal information and parameter efficiency of the proposed stacked long short-term memory (LSTM) model over single LSTMs.