Introducing A Deep Learning for Anomaly Detection in Urban Road Traffic Networks

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Presented at CRIS 2020 by

Jamal Department

This paper offers an overview of essential concepts in deep learning related cyber security in autonomous vehicle network, one of the state of the art approaches in machine learning, in terms of its history and current applications as a brief introduction to the subject. Deep learning has shown great successes in many domains such as image recognition, object detection, etc. Various forecasting schemes have been proposed to manage urban road traffic data, which is collected by different sources such as, videos cameras, sensors, Lidar, GNSS and mobile phone services. However, these are not sufficient for the purpose because of their limited coverage and high costs of installation and maintenance, which makes it difficult to detect the anomalies in collected big data. The deep learning mutiple layer leads to extract the features of the input data. The feature of input data includes normal and abnormal data. Abonormal data is cause by threats and attacks such as denial of dervice, Malicious and wrong setup which can cause an anomaly and system failure. An anomalies in automous vehicle networks causes traffic congestion and accident that have impact on the economic, environment, human and time costly. The introduction of the deep learing aims to detect the anomaly in autonomous vehicle network.