Social Psychology Inspired Distributed Ledger Technique for Anomaly Detection in Connected Vehicles
Social Psychology Inspired Distributed Ledger Technique for Anomaly Detection in Connected Vehicles
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DOI：
10.1109/tits.2023.3262398
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发表时间：
2023-07
影响因子：
8.5
通讯作者：
Heena Rathore;Siva Sai;Akshay Gundewar
中科院分区：
文献类型：
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作者：
Heena Rathore;Siva Sai;Akshay Gundewar
联网车辆 (CV) 是未来智能交通系统不可或缺的一部分，它使用通信和传感技术在车辆和基础设施之间进行通信。然而，随着车辆相互连接，其组件对异常和蓄意恶意活动的脆弱性增加。在这两种情况下，在决策过程中检测和排除异常数据至关重要。虽然深度学习技术由于其适应性而在异常检测中越来越受欢迎，但它们的计算成本很高并且需要很长的训练时间。为了克服这一挑战，本文使用基于有向无环图（DAG）的分布式账本技术，并将其与能力、诚信和仁慈的社会心理学原理相结合来计算车辆的声誉。我们引入了恶意概率，这是一种质量度量，它是误差测量（真实值和报告值之间）和声誉度量的函数。我们在上坡、环路、入口匝道、出口匝道和道路工程等道路拓扑上的智能驾驶员模块框架的模拟数据中引入了各种异常，例如偏差、噪声、短路、多短路、漂移、多漂移、卡住和寄生链攻击，以验证所提出的框架在识别异常方面的有效性。仿真结果表明，恶意因子可以作为自动确定 CV 网络异常类型的有效指标。
Connected Vehicles (CVs), an integral part of the future of intelligent transportation systems, use communication and sensing technologies to communicate among vehicles and infrastructure. However, as vehicles become interconnected, the vulnerability of their components to anomalies and deliberate malicious activity increases. In both cases, it is vital to detect and exclude anomalous data from the decision-making process. While deep learning techniques are gaining popularity for anomaly detection due to their adaptability, they are computationally expensive and require long training times. To overcome this challenge, this paper uses a directed acyclic graph (DAG) based distributed ledger technique and combines it with social psychology principles of ability, integrity, and benevolence to calculate the reputation of vehicles. We introduce the probability of malevolence, a measure of quality, which is a function of the error measurements (between ground truth and reported values) and reputation metrics. We introduce various anomalies such as bias, noise, short, multi-short, drift, multi-drift, stuck-at, and parasite chain attack in the simulated data from the Intelligent Driver Module framework on road topology such as uphill, ring, on-ramp, off-ramp, and road-works to validate the efficacy of the proposed framework in identifying the anomalies. Simulation results show that the malevolence factor serves as an efficient metric for automatically determining the types of anomalies in the CV network.