Securing smart vehicles from relay attacks using machine learning

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

doi:10.1007/s11227-019-03049-4

Cited by 39 publications

(7 citation statements)

References 24 publications

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“…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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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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“…The ML techniques are classified for the vehicular network as communication, networking, and security perspective emphasizing the future of vehicle network that include 6 G and AI techniques. Usman et al (2020) investigated ML techniques' introduction to identifying relay attacks against PKES systems. The decision tree, support vector machine, and k-nearest neighbor's methods were compared for the experiment utilizing a three-month log of the PKES system.…”

Section: Intelligent Connectivitymentioning

confidence: 99%

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

Vermesan

John

Pype

et al. 2021

Front. Future Transp.

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The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.

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“…Educational institutions require students to complete their language classes individually in multiple data classrooms focused on the English communication model in machine learning. Students collaborate with teachers using a computer system and a database for learning activities (Ahmad et al, 2020). Besides data analysis, teachers can understand each student's learning speed, common grammar mistakes, sentence structure of vocabulary and spoken levels enabling teachers to combine guidance on the automatic scoring system with common errors and problems in English learning for students.…”

Section: Significant Of Machine Learning Based English Teaching For S...mentioning

confidence: 99%

“…Have students reflect on their progress with the ability to listen and to speak. The second step is to evaluate the data in repetitive detailed analysis to determine a critical teaching point (Ahmad et al, 2020). The third phase is student information data collection (Khan et al, 2020).…”

mentioning

confidence: 99%

An English teaching quality evaluation model based on Gaussian process machine learning

et al. 2021

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Background The efficiency of conventional English teaching quality evaluation is comparatively small, and evaluation statistics are challenging. To investigate the use of artificial intelligence (AI) technology in teacher teaching assessment, a machine learning algorithm is proposed to create a teaching evaluation model suitable for the current educational model to assist colleges and universities in overcoming existing teaching challenges. Objectives The proposed Machine learning‐based Gaussian process model (MLGPM) improves the student's language skills. The proposed model uses Gaussian mixed model to express the circulation features of samples and enhances the support vector machine. Therefore, this paper suggests an active learning algorithm that, in association with Gaussian mixed model and sparse Bayesian learning, strategically chooses and labels samples to construct a classifier that syndicates the distribution characteristics of the samples. As a result, the accuracy of a considered quality index for English classrooms is verified, and the quality and control of English as a foreign language can be enhanced. Results The experiment results show that the model presented in this study is effective and beneficial when assessing the efficiency of teaching in universities and analyzing big data sets. Conclusion The simulation analysis with student performance improvement in English teaching quality using machine learning high fluency rate of 95.3, high accuracy ratio of 98.1%, improve vocabulary prediction ratio of 94.6%, improve passage prediction ratio of 92.7%, enhance learning rate of 95.2%, reduce the error rate of 24.1%, F1‐score of 91.5% and assessment score of 92.1% when compared with other methods.

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Securing smart vehicles from relay attacks using machine learning

Cited by 39 publications

References 24 publications

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

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

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

An English teaching quality evaluation model based on Gaussian process machine learning

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