Alsulami, A.A.; Abu Al-Haija, Q.; Alqahtani, A.; Alsini, R. Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model. Symmetry2022, 14, 1450.
Alsulami, A.A.; Abu Al-Haija, Q.; Alqahtani, A.; Alsini, R. Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model. Symmetry 2022, 14, 1450.
Alsulami, A.A.; Abu Al-Haija, Q.; Alqahtani, A.; Alsini, R. Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model. Symmetry2022, 14, 1450.
Alsulami, A.A.; Abu Al-Haija, Q.; Alqahtani, A.; Alsini, R. Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model. Symmetry 2022, 14, 1450.
Abstract
Nowadays, technological advancement has transformed traditional vehicles into Au-tonomous Vehicles (A.V.s). In addition, in our daily lives, A.V.s play an important role since they are considered an essential component of smart cities. A.V. is an intelligent vehicle capable of main-taining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, A.V.s collect information about the outside environment using sensors to ensure safe navigation. Furthermore, A.V.s reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, A.V.s could be threatened by cyberattacks, posing risks to human life. For example, re-searchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW AVs. Therefore, more research is needed to detect cyberattacks targeting the components of A.V.s to mitigate their negative consequences. This research will contribute to the security of A.V.s by detecting cyberattacks at the early stages. First, we inject False Data Injection (FDI) attacks into an A.V. simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify FDI attacks targeting the control system of the A.V. through a compromised sensor. We use long short-term memory (LSTM) deep networks to detect FDI attacks in the early stage to ensure the stability of the operation of A.V.s. Our method classifies the collected dataset into two classifications: normal and anomaly data. The ex-perimental result shows that our proposed model's accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.
Keywords
Autonomous vehicles (A.V.); Anomaly Detection (A.D.); Deep Learning (DL), Symmetry; Long Short-Term Memory (LSTM); False Data Injection (FDI) Attacks
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
