Intelligent Transportation System (ITS) is one of the newest technologies in the transportation sector that will give hope for better driving safety. Not only in terms of driving safety, but ITS will give also hope for driving comfort. Smart vehicles perchance better versatile to the street circumstances through trade data among vehicles. In case, they can maintain a strategic distance from activity blockage, perilous deterrents, or see activity mishaps prior. The innovation which is meticulously associated with the security of the driver must get extraordinary consideration. V2V-Vehicle-to-Vehicle connection can undermine impedance and indeed attack or anomaly. Many studies have been carried out to address this problem. The primary step is to reinforce the system's capacity to identify anomalies on Vehicular Network. Further, the growing development of machine learning seems to bring hope to support these steps. Within the proposed method, the original of our approach consists in utilizing 2-Step of anomaly detection. This framework is utilizing two classifiers machine learning from two altered preparing data-sets. We appear that the proposed method can make strides essentially attack detection achievement, compared to arrangements depending on a single detection step.

中文翻译：

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基于2步深度学习框架的车辆网络异常检测


智能交通系统（ITS）是交通领域的最新技术之一，它将为提高驾驶安全带来希望。不仅在驾驶安全方面，ITS也将给驾驶舒适性带来希望。通过车辆之间的交易数据，智能车辆可能会更好地适应街道环境。以防万一，他们可以与活动阻碍、危险的威慑保持战略距离，或者提前发现活动事故。与驾驶员安全密切相关的创新必须得到特别考虑。 V2V 车对车连接可能会破坏阻抗，甚至引发攻击或异常。已经进行了许多研究来解决这个问题。首要步骤是增强系统识别车辆网络异常的能力。此外，机器学习的不断发展似乎为支持这些步骤带来了希望。在所提出的方法中，我们方法的初衷在于利用两步异常检测。该框架利用两个分类器从两个不同的准备数据集进行机器学习。我们认为，与依赖于单个检测步骤的安排相比，所提出的方法可以在本质上取得攻击检测成果的进步。