ArticleSensors (Basel, Switzerland)2022
Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 117 citations in OpenAlex.
- Optimal attention deep learning based in-vehicle intrusion detection and classification model on CAN messages.Scientific reports · 2025Article
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- Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies.Sensors (Basel, Switzerland) · 2024Review
- IoT empowered smart cybersecurity framework for intrusion detection in internet of drones.Scientific reports · 2023Article
- CANAttack: Assessing Vulnerabilities within Controller Area Network.Sensors (Basel, Switzerland) · 2023Article
- Emerging Sensor Communication Network-Based AI/ML Driven Intelligent IoT.Sensors (Basel, Switzerland) · 2023Article
- Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms.Sensors (Basel, Switzerland) · 2023Article
- Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning: A Comparative Performance Study.Sensors (Basel, Switzerland) · 2023Article
- Network Anomaly Intrusion Detection Based on Deep Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- In-vehicle network intrusion detection systems: a systematic survey of deep learning-based approaches.PeerJ. Computer science · 2023Article
- In-Vehicle Communication Cyber Security: Challenges and Solutions.Sensors (Basel, Switzerland) · 2022Review
- Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments.Sensors (Basel, Switzerland) · 2022Article
- Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective.Sensors (Basel, Switzerland) · 2022Article
- Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices.Sensors (Basel, Switzerland) · 2022Article
- Application of Intelligent Taste Analysis Based on Random Forest Algorithm in Food Quality Inspection.Computational intelligence and neuroscience · 2022Article
- Cleanup Sketched Drawings: Deep Learning-Based Model.Applied bionics and biomechanics · 2022Article
- Computational Intelligence Approaches in Developing Cyberattack Detection System.Computational intelligence and neuroscience · 2022Article
- Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia.PeerJ. Computer science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is controlled by the controller area network (CAN) bus protocol. Nevertheless, the autonomous vehicle network still has issues and weaknesses concerning cybersecurity due to the complexity of data and traffic behaviors that benefit the unauthorized intrusion to a CAN bus and several types of attacks. Therefore, developing systems to rapidly detect message attacks in CAN is one of the biggest challenges. This study presents a high-performance system with an artificial intelligence approach that protects the vehicle network from cyber threats. The system secures the autonomous vehicle from intrusions by using deep learning approaches. The proposed security system was verified by using a real automatic vehicle network dataset, including spoofing, flood, replaying attacks, and benign packets. Preprocessing was applied to convert the categorical data into numerical. This dataset was processed by using the convolution neural network (CNN) and a hybrid network combining CNN and long short-term memory (CNN-LSTM) models to identify attack messages. The results revealed that the model achieved high performance, as evaluated by the metrics of precision, recall, F1 score, and accuracy. The proposed system achieved high accuracy (97.30%). Along with the empirical demonstration, the proposed system enhanced the detection and classification accuracy compared with the existing systems and was proven to have superior performance for real-time CAN bus security.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.