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Article
Telecommunications
Shuji Ohira et al.
Summary: A Controller Area Network (CAN) is a communication protocol used in vehicle networks, which lacks authentication and encryption, making it vulnerable to cyberattacks. To address this issue, we propose an Intrusion Detection System (IDS) using a Convolutional Neural Network (CNN) trained on recurrence images to capture the temporal dependency in the communication sequence. The proposed method achieves high accuracy in different types of attacks.
VEHICULAR COMMUNICATIONS
(2022)
Article
Telecommunications
Wei Lo et al.
Summary: This research proposes a hybrid deep learning-based intrusion detection system for accurately characterizing in-vehicle network traffic, achieving high detection accuracy and low false alarm rate in different types of cyber-attacks.
VEHICULAR COMMUNICATIONS
(2022)
Proceedings Paper
Computer Science, Information Systems
Dorsaf Swessi et al.
Summary: This paper investigates the detection of Fuzzy attacks in the internal vehicle network using ensemble learning techniques. The efficiency of these techniques was evaluated on realistic datasets and a new advanced stealthy attack dataset with physical impacts on the vehicle. The results show that eXtreme, Light, and Category Gradient Boosting, as well as Bagging ensemble learning techniques, significantly improve the detection performance in terms of accuracy, training and testing time reduction, and decreased false alarm rate.
ADVANCED INFORMATION NETWORKING AND APPLICATIONS, AINA-2022, VOL 2
(2022)
Article
Computer Science, Hardware & Architecture
Yuanda Yang et al.
Summary: Controller area network (CAN) bus-based connected and even self-driving vehicles face severe cybersecurity challenges due to external connections and vulnerabilities, leading to privacy and security threats. Generative adversarial nets (GAN)-based intrusion detection systems (IDSs) can overcome limitations of insufficient attack data types in deep learning-based IDSs. This study proposes an improved GAN-based intrusion detection method for in-vehicle networks, which enhances evaluation metrics and reduces detection time by utilizing a new loss function and sparse enhancement training method.
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
(2021)
Article
Telecommunications
Hongmao Qin et al.
Summary: Electronization and intelligentization are becoming the fundamental characteristics of modern automobiles. Automotive information security is increasingly highlighted with the deepening of intelligent network integration. An anomaly detection algorithm based on long short-term memory (LSTM) is proposed to detect abnormal behavior of the controller area network (CAN) bus, showing lower false positive rate and higher detection rate.
VEHICULAR COMMUNICATIONS
(2021)
Article
Computer Science, Theory & Methods
Emad Aliwa et al.
Summary: The increasing connectivity within vehicles has raised concerns about safety and security, especially regarding automotive serial protocols like CAN Bus. Current security mechanisms focus on encryption, authentication, and IDS, but face limitations due to constraints like low bandwidth and real-time sensitivity. Future research needs to address these limitations and challenges in order to improve the security of connected vehicles.
ACM COMPUTING SURVEYS
(2021)
Review
Computer Science, Information Systems
Kyounggon Kim et al.
Summary: With the development of smart technology, cities are becoming increasingly intelligent, and smart mobility is a crucial element in smart cities. Autonomous vehicles, as an essential part of smart mobility, may pose threats to quality of life and human safety due to vulnerabilities. Security researchers have studied attacks and defenses for autonomous vehicles, but there is a lack of systematic research in this area.
COMPUTERS & SECURITY
(2021)
Article
Telecommunications
Hwanseok (Harrison) Jeong et al.
Summary: The paper discusses safe and efficient driving in smart transportation, focusing on systems, protocols, applications, and security for autonomous vehicles. Monitoring road surfaces and identifying hazards are essential for smart transportation, requiring vehicles to share sensor information through wireless communication. The combination of vehicular networking and navigation enhances driving safety and increases traffic efficiency.
VEHICULAR COMMUNICATIONS
(2021)
Editorial Material
Engineering, Civil
Alireza Jolfaei et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Civil
Guoqi Xie et al.
Summary: With the advancement of Internet of vehicles and autonomous driving technologies, automotive Controller Area Networks (CAN) face various security threats. A enhanced deep learning GAN model is proposed to address this issue, which improves detection accuracy of data tampering threat by designing elaborate CAN message blocks and enhancing GAN discriminator.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Telecommunications
Jaehoon (Paul) Jeong et al.
Summary: This paper explores IP-based vehicular networking in smart road scenarios, discussing background, technologies, architecture, addressing, and mobility management, while analyzing use cases and research challenges in V2I, V2V, and V2X communications.
VEHICULAR COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Ruijie Zhao et al.
Summary: The rapid growth of wireless sensor networks and applications has led to an increase in unsolicited intrusions and security threats, disrupting normal operations. Deep learning-based network intrusion detection methods have been widely studied, but their high computational complexity hinders deployment in devices with limited processing power. This letter introduces a lightweight dynamic autoencoder network (LDAN) for NID, which efficiently extracts features through a lightweight structure design. Experimental results demonstrate that the proposed model achieves high accuracy and robustness while significantly reducing computational cost and model size.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2021)
Article
Computer Science, Information Systems
Tarek Moulahi et al.
Summary: This paper investigates the use of multiple ML techniques to detect intrusions in in-vehicle communication, with experimental results showing that the techniques under consideration outperform other previously used methods.
Article
Engineering, Civil
Wufei Wu et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2020)
Article
Telecommunications
Hyun Min Song et al.
VEHICULAR COMMUNICATIONS
(2020)
Article
Computer Science, Information Systems
Shahroz Tariq et al.
COMPUTERS & SECURITY
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Yuwei Sun et al.
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2020)
Article
Computer Science, Information Systems
Markus Hanselmann et al.
Article
Computer Science, Theory & Methods
Bogdan Groza et al.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2019)
Article
Computer Science, Information Systems
George Loukas et al.
Article
Computer Science, Information Systems
Haojie Ji et al.
Review
Telecommunications
Sparsh Sharma et al.
VEHICULAR COMMUNICATIONS
(2018)
Proceedings Paper
Computer Science, Information Systems
Hyunsung Lee et al.
2017 15TH ANNUAL CONFERENCE ON PRIVACY, SECURITY AND TRUST (PST)
(2017)