Tire Pressure Monitoring System encryption to improve vehicular security

Kilcoyne, Deirdre K.; Bendelac, Shiri; Ernst, Joseph M.; Michaels, Alan J.

doi:10.1109/milcom.2016.7795497

Cited by 11 publications

(5 citation statements)

References 4 publications

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“…Because intelligent algorithms are widely applied in onboard sensing systems to improve control performance, adversaries can use traditional machine learning algorithms like k-nearest neighbor (KNN) or conventional deep learning algorithms like deep neural network (DNN) to conduct sensing level algorithm attacks [13][14][15]. In addition to the abovementioned raw data attack and intelligent algorithm attacks for in-vehicle sensors, more malicious cyber characteristics attacks also occur with the continuous transmission of sensor data, which signal attacks can mimic a sensor to conduct spoofing or jamming attack [16], and wireless signal attacks can listen and inject spoof message to cheat a central node in a victim in-vehicle TPMS [17].…”

Section: Related Workmentioning

confidence: 99%

Resilient Recovery for In-vehicle Sensor Attack by LSTM

Ye¹

2022

Preprint

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<div>Uncertain cyber-attack detection and effective recovery after attacks on in-vehicle sensors are a challenge in the domain of the Internet of Vehicles (IoV). Currently, modern automatic vehicles are equipped with a variety of low-cost sensors with relatively limited computation capabilities, which constrains state-of-the-art solutions. As a result of the embedded intelligence chip in the IoV devices, deep learning algorithms can be applied to provide a novel solution for sensor attack detection and recovery. This paper is concerned with the resilient recovery after attacking the in-vehicle reference-free sensor. Toward this aim, we firstly define a theoretical model to describe automatic ground vehicles with vehicular yaw rate and local state. Then, classical attack models are established to transfer the challenge to a convex optimization issue. Finally, we consider evaluating the performance of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and LSTM optimized by CNN (CNN-LSTM) for the general in-vehicle sensor units attack detection and recovery. The evaluation metrics for comparison experiments are accuracy, Receiver Operating Characteristic (ROC) curve, and the confusion matrix. The proposed CNN-LSTM model can obtain 95% testing accuracy, which is better than 56% for CNN and 79% for LSTM in detecting attacks. The predicting experimental results support that the LSTM is suitable for recovering signals of in-vehicle sensors.</div>

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Section: Related Workmentioning

confidence: 99%

Resilient Recovery for In-vehicle Sensor Attack by LSTM

Ye¹

2022

Preprint

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“…To protect the identity, one option is to encrypt the uniquely identifying number broadcasted in messages. For example, in a TPMS encrypting the per sensor identifier while leaving the rest of the message unencrypted protects the identity and facilitates issue diagnosis by humans due to the unencrypted contents [61]. Each time a message is broadcast a different encrypted value would be sent, essentially making it appear as if a random identifier was being used.…”

Section: B Perturbing Identitymentioning

confidence: 99%

“…This means that the message contents can still be used by existing tools, meaning both backwards compatibility and privacy are provided. To obtain a stronger encryption the authors of [61] propose the encrypted identifier be lengthened from 32 bit to 64 bit, but this would break backwards compatibility. This technique works for TPMS because the sender and receiver are only a single communication hop away from each other, and hardware deployers can ensure the vehicle is aware of what TPMS identifiers to expect and how they will be encrypted.…”

Section: B Perturbing Identitymentioning

confidence: 99%

Privacy Challenges With Protecting Live Vehicular Location Context

et al. 2020

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“…Emura et al [2] have examined sensor costs and have demonstrated a protocol that could be used under current TPMS constraints. Other researchers [4,8] have shown that rolling IDs that change between TPMS transmissions are feasible and can defeat tracking methods. The next generation of TPMSs may incorporate these and other upgrades.…”

Section: Securitymentioning

confidence: 99%

Vehicle Identification and Route Reconstruction via TPMS Data Leakage

Hacker

Graham

Dunlap

2019

IFIP Advances in Information and Communication Technology

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Tire pressure monitoring systems have become a mandatory feature of modern automobiles, but their presence opens a new attack vector for a potential adversary. These systems have minimal security features, allowing for eavesdropping and data injection with low technical and financial costs.This chapter explores the potential for tire pressure monitoring systems to provide inputs to a remote sensing network, which leverages the data broadcast by the systems to identify vehicles and track their movements. A traffic simulation is employed to generate vehicle movements and tire pressure monitoring system packets. Experiments demonstrate that the tire pressure monitoring system data can help identify vehicles and reconstruct vehicle routes. They show that a determined adversary could deploy sensors to detect tire pressure monitoring systems and learn about the movements of individual vehicles without any insider information. Potential solutions to this privacy problem are discussed, focusing on low cost changes with the greatest consumer security benefits.

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Tire Pressure Monitoring System encryption to improve vehicular security

Cited by 11 publications

References 4 publications

Resilient Recovery for In-vehicle Sensor Attack by LSTM

Resilient Recovery for In-vehicle Sensor Attack by LSTM

Privacy Challenges With Protecting Live Vehicular Location Context

Vehicle Identification and Route Reconstruction via TPMS Data Leakage

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