Abstract
Recently, 5G networks have emerged as a new technology that can control the advancement of telecommunication networks and transportation systems. Furthermore, 5G networks provide better network performance while reducing network traffic and complexity compared to current networks. Machine-learning techniques (ML) will help symmetric IoT applications become a significant new data source in the future. Symmetry is a widely studied pattern in various research areas, especially in wireless network traffic. The study of symmetric and asymmetric faults and outliers (anomalies) in network traffic is an important topic. Nowadays, deep learning (DL) is an advanced approach in challenging wireless networks such as network management and optimization, anomaly detection, predictive analysis, lifetime value prediction, etc. However, its performance depends on the efficiency of training samples. DL is designed to work with large datasets and uses complex algorithms to train the model. The occurrence of outliers in the raw data reduces the reliability of the training models. In this paper, the performance of Vehicle-to-Everything (V2X) traffic was estimated using the DL algorithm. A set of robust statistical estimators, called M-estimators, have been proposed as robust loss functions as an alternative to the traditional MSE loss function, to improve the training process and robustize DL in the presence of outliers. We demonstrate their robustness in the presence of outliers on V2X traffic datasets.
Highlights
The evolution of 5G networks is characterized by a multilayer nature, high complexity, low latency, large bandwidth, high capacity, and heterogeneity
When using noise-free data, the robust Fair loss function performs well and has the best performance compared to its peers
When using data corrupted by Gaussian noise, the robust Cauchy loss function shows the best performance compared to the others
Summary
The evolution of 5G networks is characterized by a multilayer nature, high complexity, low latency, large bandwidth, high capacity, and heterogeneity. The wide distribution of sensitive medical information in IoT healthcare systems leaves them vulnerable to complex attacks that aim at main security aspects such as privacy and safety. The massive increase in data generated by vehicular networks makes vehicular communication vulnerable to attacks such as anomalies or outliers These are critical factors that affect traffic flow and network security and impact the global economy. In implementing the robust algorithms, ML requires close monitoring and the necessary measures to narrow down the scope, understanding, and threat model [18,19] In many fields, such as industrial modeling, multilayer feedforward neural networks (MFNNs) or deep neural networks (DNNs) are used as approximators of nonlinear functions to provide advanced solutions for various applications. Outline of the article: The article is structured : the related literature review is presented in Section 2; the proposed work is explained in Section 3; the robust learning and outliers are introduced in Section 4; the basic concepts of M-estimator loss function are defined in Section 5; V2X simulation environment is introduced in Section 6; Deep Neural Network Learning is presented in Section 7; our theoretical results are illustrated in Section 8; and in Section 9 we conclude
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