A Data Generation Workflow for Consensus-Based Connected Vehicle Security
A Data Generation Workflow for Consensus-Based Connected Vehicle Security
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DOI：
10.1109/icce56470.2023.10043181
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Henry Griffith;Malik Farooq;Heena Rathore
中科院分区：
文献类型：
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作者：
Henry Griffith;Malik Farooq;Heena Rathore
由于其可扩展性和集成到现有通信协议中的潜力，基于共识的信任算法是检测互联车辆网络中数据伪造攻击的一种很有前途的方法。尽管有这种潜力，但支持这些算法开发所需的标准化数据集并不容易获得。该手稿展示了通过开发一个合成数据生成框架来解决这一限制的进展，该框架1）模拟在BSM广播的真实的数据集内生成对等估计的基本安全消息（BSM），以及2）通过将异常注入对等估计和自我报告的BSM信号来模拟网络内恶意节点的存在。
Due to their scalability and potential to integrate into existing communication protocols, consensus-based trust algorithms are a promising approach for detecting data falsification attacks in connected vehicle networks. Despite this potential, the standardized data sets necessary to support the development of these algorithms are not readily available. This manuscript demonstrates progress towards addressing this limitation by developing a synthetic data generation framework which — 1) simulates the generation of peer-estimated basic safety messages (BSM) within a real data set of BSM broadcasts, and 2) simulates the presence of malicious nodes within the network by injecting anomalies into peer-estimated and self-reported BSM signals.