Developing a machine learning-based algorithm for secure routing in autonomous vehicles

Abstract

Over the last several years, we have noticed that the administration and car companies have been refraining from investing capital in connected autonomous cars. A chain of interconnected sensors, actuators, and devices—combining lidar, GPS, radar, in-system machines, cameras, and more—allows autonomous cars to learn how to operate in real-time scenarios. However, data from recent cyberattacks and mishaps suggests that malicious attackers can access this sensor data or even the main control system through hacking, leading to a variety of losses. As time passes by, people will be less interested in connected autonomous vehicles (CAVs), and various surveys say that it will be mainly because of the absence of a proper security infrastructure. A study of cyberattacks on CAVs and strategies to eliminate them is discussed in this study. In this study, we have proposed a hybrid model solution that uses convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), and we are optimizing the Hyperparameters using the dwarf mongoose optimization algorithm (DMOA). The proposed model is also tested in Comparison with other existing models, and we are able to detect DoS, fuzzy, spoofing the driving gear, and spoofing the RPM gauge attacks with an accuracy of more than 98%.