Online transportation network cyber-attack detection based on stationary sensor data
Online transportation network cyber-attack detection based on stationary sensor data
复制标题
DOI：
10.1016/j.trc.2023.104058
复制
发表时间：
2023-04
期刊：
影响因子：
--
通讯作者：
Ruixiao Sun;Qi Luo;Yuche Chen
中科院分区：
文献类型：
--
作者：
Ruixiao Sun;Qi Luo;Yuche Chen
联网车辆和众包地图应用的普及导致交通网络存在安全漏洞。准确检测对交通网络的网络攻击对于最大限度地减少对交通系统的影响至关重要。这项任务特别具有挑战性，因为区域网络攻击的影响可能对汇总的交通数据是不可见的，特别是当交通机构只能访问传感器数据时。我们提出了一个分析框架，利用实时道路链路传感数据进行在线数据驱动的交通网络异常检测使用非参数长短期记忆（LSTM）和参数高斯过程模型。在线异常检测模型可以在实时传感数据到达时连续更新模型系数。我们利用城市规模的微观交通模拟来验证我们的网络攻击检测框架。网络攻击检测模型实现了F1得分，这是分类器的精确度和召回率的调和平均值，在考虑不同初始训练数据大小的情况下，在84%到96%之间。我们与主要的离线模型进行比较，以证明在线模型的有效性和鲁棒性。此外，我们设计了一个元启发式方法来解决多目标传感器定位问题，同时提高异常检测效率和最大限度地提高交通信息增益。这项研究展示了一种系统的方法来解决交通网络中新出现的网络安全问题，并对基础设施升级提出最低要求。我们的研究结果可以帮助交通安全部门识别潜在的网络攻击，并保护交通基础设施免受恶意网络黑客的攻击。
Penetration of connected vehicles and crowdsourced mapping applications give rise to security vulnerabilities in transportation networks. Accurate detection of cyber-attacks on transportation networks is critical to minimize impacts on transportation systems. This task is particularly challenging because the impacts of regional cyber-attacks can be invisible on aggregated traffic data, especially when only sensor data is accessible to transportation agencies. We propose an analytical framework that leverages real-time road link sensory data to conduct online data-driven transportation network anomaly detection using non-parametric long short-term memory (LSTM) and parametric Gaussian process model. The online anomaly detection models can continuously update model coefficients as real-time sensory data arrives. We utilize a city-scale microscopic traffic simulation to validate our cyber-attack detecting framework. The cyber-attack detection model achieves a F 1 score, which is a harmonic mean of the precision and recall of classifiers, between 84% to 96% considering different initial training data sizes. We compare with major offline models to demonstrate the effectiveness and robustness of online models. In addition, we devised a meta-heuristic method to solve the multi-objective sensor location problem to simultaneously enhance anomaly detection efficiency and maximize traffic information gain. This study demonstrates a systematic approach to address the emerging concerns of cyber-security in transportation networks with minimum requirements for infrastructure upgrades. Our results can help transportation security authorities identify potential cyber-attacks and protect transportation infrastructure from malicious cyber-hackers.