TSP Security in Intelligent and Connected Vehicles: Challenges and Solutions

Li, Yansong; Luo, Qian; Li, Jiajia; Guo, Hongzhi; Kato, Nei

doi:10.1109/mwc.2019.1800289

Cited by 71 publications

(17 citation statements)

References 10 publications

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“…This is partially due to the lack of variety and public familiarity with operating systems like Linux, Green Hills, Windows CE, and QNX, which allow hackers to transfer knowledge from computers to vehicles (Li et al 2018). Also, OEM telematics service providers allow several avenues for infotainment systems to access and interact with the internet, bringing with them interconnectivity vulnerabilities (Li et al 2019). The infotainment systems in most modern and future vehicles aggregate and display data about the current status of various functionalities and consistently update information; however, the connectivity, configuration, and custom exploits related to infotainment systems vary from manufacturer to manufacturer, model to model, and year to year (Rohde 2019).…”

Section: Remote Access Risksmentioning

confidence: 99%

“…Many modern vehicles house a device called a telecommunications box (T-Box) that stores information gathered from external sources and sensors generated in the vehicle. The central CAN bus of the vehicle creates an all-inclusive network environment by connecting the vehicle's OBD II port, T-Box, and infotainment system (Li et al 2019); this port has direct access to the vehicle's ECUs. Information from the T-Box is capable of being transmitted through a SIM card linked to a cellular connection capability in the vehicle for OTA update reception and diagnostics.…”

Section: Telematicsmentioning

confidence: 99%

“…However, the most threatening method for controlling a vehicle through telematics would be to reflash the firmware remotely, which could give a malicious actor insight, control, and ability to manipulate functions of the vehicle as they desire. Research has shown the ability to reflash aftermarket telematics firmware (Foster et al 2015) and incorporate malicious code into OEM telematics (Li et al 2019) when critical security measures were not instituted. These failures included using the same cryptographic key for every telematics device, a lack of strong authentication procedures, lack of encryption, and an unsecured update server.…”

Section: Remote Access Risksmentioning

confidence: 99%

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Vehicle Cybersecurity Threats and Mitigation Approaches

Hodge

Hauck

Gupta

et al. 2019

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Section: Remote Access Risksmentioning

confidence: 99%

Section: Telematicsmentioning

confidence: 99%

Section: Remote Access Risksmentioning

confidence: 99%

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Vehicle Cybersecurity Threats and Mitigation Approaches

Hodge

Hauck

Gupta

et al. 2019

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“…A large number of user's privacy-sensitive information is included in the Internet of Vehicles. Once leaked, it may cause serious consequences [1]. With the rapid development of 5G [2] [3], big data, artificial intelligence [4], and blockchains [5], more and more attentions are paid to user's privacy and security [6] in the Internet of Vehicles.…”

Section: Introductionmentioning

confidence: 99%

The Intelligent Offense and Defense Mechanism of Internet of Vehicles Based on the Differential Game-IP Hopping

et al. 2020

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The Road Side Unit (RSU) is an important part of the Internet of Vehicles (IoV), which collects a large number of user's privacy information and connects vehicles to the Internet. The current RSU is running with static configurations, and the adversary can get enough time to analyze the vulnerability of the RSU and launch attacks, which poses a huge threat to the privacy security of the IoV. Under this asymmetrical condition, the attackers occupy a predominant position, and it is difficult to fully defend. To solve the above asymmetry offense-defense problem, we adopt the moving target defense (MTD) to dynamically change the IP address of the RSU, which increases the difficulty of the attacker's attack. However, due to the unreasonable selection of the RSU's IP hopping frequency, it is difficult for the RSU's IP hopping to exert its defense effect or it may introduce excessive defense overheads. Given the above issue, we propose an intelligent offense and defense mechanism based on a differential game approach, which can adaptively adjust the RSU's IP hopping frequency and maximize its defense benefits. First, based on the offense and defense relationship, we model the RSU's IP hopping frequency intelligent arrangement as a differential game (i.e., IP hopping game) in which the attacker attempts to maximize his potential benefits, and the RSU manages to maximize its defense benefits. On this basis, we calculate a potential Nash equilibrium of the IP hopping game. Finally, simulation results show our proposed mechanism can adaptively adjust the RSU's IP hopping frequency according to the attack frequency and effectively defend attacks. Compared with the mechanisms with a fixed IP hopping frequency, our proposed mechanism can make up for the ineffectiveness of the fixed low-frequency IP hopping defense and avoid the excessive defense overheads of the fixed high-frequency IP hopping defense. INDEX TERMS Internet of vehicles, privacy security, moving target defense, IP hopping, a differential game.

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“…I N FUTURE Intelligent Transportation Systems (ITSs), vehicles will be equipped with more advanced sensors to monitor surrounding driving environments, driving behaviors and vehicle status, and share the collected data through vehicular networks to facilitate traffic management and improve driving experience [1], [2]. On the other hand, passengers in vehicles also desire wireless connections to enjoy high-quality mobile services just as at home.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Handovers for Sub-6 GHz and mmWave Integrated Vehicular Networks

Ding

Zhang

et al. 2019

IEEE Trans. Wireless Commun.

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The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles' positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to

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TSP Security in Intelligent and Connected Vehicles: Challenges and Solutions

Cited by 71 publications

References 10 publications

Vehicle Cybersecurity Threats and Mitigation Approaches

Vehicle Cybersecurity Threats and Mitigation Approaches

The Intelligent Offense and Defense Mechanism of Internet of Vehicles Based on the Differential Game-IP Hopping

Machine Learning-Based Handovers for Sub-6 GHz and mmWave Integrated Vehicular Networks

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