Transfer learning based intrusion detection scheme for Internet of vehicles
Transfer learning based intrusion detection scheme for Internet of vehicles
复制标题
基于迁移学习的车联网入侵检测方案
DOI：
10.1016/j.ins.2020.05.130
复制
发表时间：
2021-02-08
影响因子：
8.1
通讯作者：
Ma, Jianfeng
中科院分区：
文献类型：
--
作者：
Li, Xinghua;Hu, Zhongyuan;Ma, Jianfeng
车联网作为一种新型网络，其攻击类型不断涌现和变化。因此，基于机器学习的入侵检测模型必须更新以科普新的攻击。然而，现有的基于机器学习的车联网入侵检测方案需要大量的标记数据来完成模型更新。对于新的攻击，车联网云也很难及时识别，这在车联网中需要大量的人力和时间成本。针对上述问题，本文采用迁移学习方法，根据车联网云能否及时为新攻击提供少量标记数据，提出了两种模型更新方案。第一种是云辅助更新方案，其中车联网云可以提供少量数据。第二种是本地更新方案，其中车联网云不能及时提供任何标记数据。在本文中，局部更新方案通过预分类获得新攻击中未标记数据的伪标签，并将伪标签数据用于多轮迁移学习。然后，车辆可以完成更新，而无需通过车联网云获取任何标记数据。实验结果表明，与现有方法相比，我们的两种方案的检测准确率至少提高了23%。(C)2020由Elsevier Inc.出版。
As a new type of network, the types of attack in the Internet of Vehicles (IoV) are constantly emerging and changing. Consequently, the machine learning based intrusion detection model has to update to cope with new attacks. However, existing machine learning based IoV intrusion detection schemes require large amounts of labeled data to complete model updates. For new attacks, the IoV cloud is also difficult to identify in time, which requires a lot of labor and time cost in IoV. To solve above issue, this paper employs transfer learning and proposes two model update schemes based on whether the IoV cloud can timely pro-vide a small amount of labeled data for a new attack. The first one is the cloud-assisted update scheme where the IoV cloud can provide a small amount of data. And the second one is the local update scheme where the IoV cloud cannot provide any labeled data timely. In this paper, the local update scheme obtains pseudo label of the unlabeled data in new attacks via pre-classifies and uses the pseudo-labeled data for multiple rounds of transfer learning. Then the vehicle can complete the update without obtaining any labeled data through the IoV cloud. The experimental results show that compared with the existing method, our two schemes have improved the detection accuracy by at least 23%. (C) 2020 Published by Elsevier Inc.