Survey: Vulnerability Analysis of Low-Cost ECC-Based RFID Protocols against Wireless and Side-Channel Attacks

Gabsi, Souhir; Beroulle, Vincent; Kieffer, Yann; Dao, Hiep Manh; Kortli, Yassin; Hamdi, Belgacem

doi:10.3390/s21175824

Cited by 10 publications

(5 citation statements)

References 89 publications

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“…Several mitigation techniques have been developed to deal with DoS attacks such as the deployment of strong authentication mechanisms, the use of physical unclonable functions (PUF) [ 25 ], the possible use of alarms triggered when a tag is being tampered with, and the constant update of the RFID devices [ 20 ]. In [ 26 ], a comparative study has been presented, which studies different RFID authentication protocols and highlights those protocols which handle DoS attacks. As for the battery drainage due to DoS attacks, strong authentication protocols will be deployed in the proposed system to try to reduce the possibility of these attacks to the minimum.…”

Section: Methodsmentioning

confidence: 99%

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Osman

Saleh

et al. 2021

IJERPH

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Since 2020, the world is still facing a global economic and health crisis due to the COVID-19 pandemic. One approach to fighting this global crisis is to track COVID-19 cases by wireless technologies, which requires receiving reliable, efficient, and accurate data. Consequently, this article proposes a model based on Lagrange optimization and a distributed deep learning model to assure that all required data for tracking any suspected COVID-19 patient is received efficiently and reliably. Finding the optimum location of the Radio Frequency Identifier (RFID) reader relevant to the base station results in the reliable transmission of data. The proposed deep learning model, developed using the one-dimensional convolutional neural network and a fully connected network, resulted in lower mean absolute squared errors when compared to state-of-the-art regression benchmarks. The proposed model based on Lagrange optimization and deep learning algorithms is evaluated when changing different network parameters, such as requiring signal-to-interference-plus-noise-ratio, reader transmission power, and the required system quality-of-service. The analysis of the obtained results, which indicates the appropriate transmission distance between an RFID reader and a base station, shows the effectiveness and the accuracy of the proposed approach, which leads to an easy and efficient tracking system.

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Section: Methodsmentioning

confidence: 99%

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Osman

Saleh

et al. 2021

IJERPH

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“…Thus, published papers either detail specific attacks [37][38][39] or develop advanced defenses [40][41][42]. Particular care was devoted to authentication protocols [19,20,[43][44][45][46], since they are the basis of the most crucial countermeasures. Previous research undoubtedly had the merit of addressing many of the possible attacks using a plethora of different techniques, whose efficacy was carefully evaluated.…”

Section: Novelty Over Previous Workmentioning

confidence: 99%

“…In the MIM case, the SPA is practically impossible to perform if the communication distance between the reader and the tag is a few centimeters, whereas it is a possible attack if a reading range of a few meters is exploited. In any case, some elliptic curve cryptography (ECC) protocols can protect the transmitted data [44]. Specifically, Liao's ECC protocol [40] exploits the Montgomery ladder as an effective countermeasure to SPA [63].…”

Section: Power Analysis Attacksmentioning

confidence: 99%

Privacy-Aware Architectures for NFC and RFID Sensors in Healthcare Applications

Raso

Bianco

Bracciale

et al. 2022

Sensors

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World population and life expectancy have increased steadily in recent years, raising issues regarding access to medical treatments and related expenses. Through last-generation medical sensors, NFC (Near Field Communication) and radio frequency identification (RFID) technologies can enable healthcare internet of things (H-IoT) systems to improve the quality of care while reducing costs. Moreover, the adoption of point-of-care (PoC) testing, performed whenever care is needed to return prompt feedback to the patient, can generate great synergy with NFC/RFID H-IoT systems. However, medical data are extremely sensitive and require careful management and storage to protect patients from malicious actors, so secure system architectures must be conceived for real scenarios. Existing studies do not analyze the security of raw data from the radiofrequency link to cloud-based sharing. Therefore, two novel cloud-based system architectures for data collected from NFC/RFID medical sensors are proposed in this paper. Privacy during data collection is ensured using a set of classical countermeasures selected based on the scientific literature. Then, data can be shared with the medical team using one of two architectures: in the first one, the medical system manages all data accesses, whereas in the second one, the patient defines the access policies. Comprehensive analysis of the H-IoT system can be useful for fostering research on the security of wearable wireless sensors. Moreover, the proposed architectures can be implemented for deploying and testing NFC/RFID-based healthcare applications, such as, for instance, domestic PoCs.

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“…• Desynchronization attack: The main goal of this type of Dos attack is to block the update of secret keys transferred between the tag and the reader. A scenario occurs when an attacker can sabotage the synchronous state between the tag and the reader by preventing the sent message updates, causing the tag and the reader to store different values [119].…”

Section: Possible Attacks Against Rfid Tagsmentioning

confidence: 99%

Analysis on Security and Privacy Guidelines: RFID-Based IoT Applications

2022

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The Internet of Things (IoT) comprises many technologies, among them is Radio Frequency Identification (RFID), which can be used to track single or multiple objects. This technology has been widely used in healthcare, supply chain, logistics, and asset tracking. However, such applications require a high level of security and privacy and are unfortunately vulnerable to various attacks and threats that need to be addressed in order for RFID-based IoT applications to reach their full potential. To this end, we propose a set of security and privacy guidelines for RFID, supported by modelling guidelines, mitigations, and the attack vectors cohesively. We compare to the state of the art and point out their shortcomings on known guidelines and reason to address these in our model. The overall methodology is as follows: (i) identify the security and privacy guideline features, (ii) highlight the security goals for RFID-based IoT applications, (iii) analyze the features in relation to RFID industrial standards, and relate them to security goals, (iv) summarize attacks and threats against RFID applications and correlate them with violated security goals, (v) derive a set of security and privacy guidelines for RFID applications in accordance with security and privacy by design frameworks. We also describe our derived guidelines in connection with the involved stakeholders, and (vi) outline the existing mitigation strategies to implement our proposed guidelines. Finally, we describe the main limitations of our work that should be investigated in the future and identify the multiple challenges that concern current security strategies.INDEX TERMS Internet of Things;RFID;security guidelines;privacy guidelines;countermeasures;security goals;privacy and security by design;attacks

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Survey: Vulnerability Analysis of Low-Cost ECC-Based RFID Protocols against Wireless and Side-Channel Attacks

Cited by 10 publications

References 89 publications

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Privacy-Aware Architectures for NFC and RFID Sensors in Healthcare Applications

Analysis on Security and Privacy Guidelines: RFID-Based IoT Applications

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