Secure Passive Keyless Entry and Start System Using Machine Learning

Ahmad, Usman; Song, Hong; Bilal, Awais; Alazab, Mamoun; Jolfaei, Alireza

doi:10.1007/978-3-030-05345-1_26

Cited by 13 publications

(3 citation statements)

References 14 publications

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“…The rapid developments in the newly created techniques, computer resources, and free software communities make ML-based technologies an essential participant in the digital structural revolution. According to a digitalization plan, the future workplace may be entirely networked and digitized and intelligent than existing production settings [12,13].…”

Section: Introduction To Cyber-physical Systemmentioning

confidence: 99%

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

Alsufyani

Alotaibi

Almagrabi

et al. 2021

Complex Intell. Syst.

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Data management is one obstacle in the production sector to be reconfigured and adapted through optimum parameterization in industry cyber-physical systems. This paper presents an intelligent data management framework for a cyber-physical system (IDMF-CPS) with machine-learning methods. A training approach based on two enhanced training procedures, running concurrently to upgrade the processing and communication strategy and the predictive models, is contained in the suggested reasoning modules. The method described spreads computational and analytical engines in several levels and autonomous modules to enhance intelligence and autonomy for controlling and tracking behavior on the work floor. The appropriateness of the suggested solution is supported by rapid reaction time and a suitable establishment of optimal operating variables for the required quality during macro- and micro-operations.

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Section: Introduction To Cyber-physical Systemmentioning

confidence: 99%

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

Alsufyani

Alotaibi

Almagrabi

et al. 2021

Complex Intell. Syst.

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show abstract

“…Other approaches have incorporated machine learning techniques to mitigate relay attacks [ 27 , 28 ]. The proposed methods utilize security features including key fob acceleration, signal strength, location, and time to achieve a high accuracy rate of 99.8%.…”

Section: Related Workmentioning

confidence: 99%

BackProx: Secure Backscatter-Assisted Proximity Detection for Passive Keyless Entry and Start Systems

Park

Hong

2023

Sensors

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A passive keyless entry and start (PKES) system is an electronic lock for an automobile that provides the great convenience of opening the door when the user is in proximity. However, the system suffers from relay attacks. Recent studies revealed that relayed signals result in valid packets that are sufficient to unlock doors. In particular, the adversary causes proximity errors by injecting a certain time delay before relaying to manipulate the phase rotation in the response signal. To this end, we present a novel relay-resilient proximity detection solution, BackProx, which uses pseudo-random frequency hopping with the assistance of a reference backscattering device. Since the relay adversary transmits the relayed signals from the key fob at long distances, the signals should propagate over longer distances, resulting in inevitable significant phase rotation with different frequencies. Inspired by this finding, BackProx uses an additional backscattering device to ensure the proximity of the key fob using the invariant characteristics of radio frequency signals in the physical layer (i.e., phase rotation). Our evaluation demonstrates the effectiveness of BackProx in resisting three types of relay attacks. The results show that it achieved a 98% true positive rate at close range and a 0.3% false positive rate at long range.

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“…In this case, the authentication mechanism of the system can be evaded by an attacker. Ahmad et al [2] presented a secure passive keyless entry and start method using machine learning. While the method supports the detection of relay attacks in a challenge-response setting between a key fob and a vehicle, it does not support mutual authentication between the devices, i.e., the key fob and the vehicle.…”

Section: Related Workmentioning

confidence: 99%

Crypto-Chain: A Relay Resilience Framework for Smart Vehicles

Sani

Yuan

Bertino

et al. 2021

Annual Computer Security Applications Conference

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Recent findings show that smart vehicles can be exposed to relay attacks resulting from weaknesses in cryptographic operations, such as authentication and key derivation, or poor implementation of these operations. Relay attacks refer to attacks in which authentication is evaded without needing to attack a smart vehicle itself. They are a recurrent problem in practice. In this paper, we formulate the necessary relay resilience settings for strengthening authentication and key derivation and achieving the secure design and efficient implementation of cryptographic protocols based on universal composability, which allows the modular design and analysis of cryptographic protocols. We introduce Crypto-Chain, a relay resilience framework that extends Kusters's universal composition theorem on a fixed number of protocol systems to prevent bypass of cryptographic operations and avoid implementation errors. Our framework provides an ideal crypto-chain functionality that supports several cryptographic primitives. Furthermore, we provide an ideal functionality for mutual authentication and key derivation in Crypto-Chain by which cryptographic protocols can use cryptographic operations, knowledge about the computation time of the operations, and cryptographic timestamps to ensure relay resilience. As a proof of concept, we first propose and implement a mutual authentication and key derivation protocol (𝑀𝐾𝐷) that confirms the efficiency and relay resilience capabilities of Crypto-Chain and then apply Crypto-Chain to fix two protocols used in smart vehicles, namely Megamos Crypto and Hitag-AES/Pro.

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Secure Passive Keyless Entry and Start System Using Machine Learning

Cited by 13 publications

References 14 publications

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

BackProx: Secure Backscatter-Assisted Proximity Detection for Passive Keyless Entry and Start Systems

Crypto-Chain: A Relay Resilience Framework for Smart Vehicles

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