RFID: An appraisal of malevolent attacks on RFID security system and its resurgence

Pandian, M. Thurai; Sukumar, R.

doi:10.1109/mite.2013.6756297

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…As the L 2 weight decay and dropout regularization are applied to constrain the learning process, the generalization error in the learning process can be reduced, and the model has a good generalization effect [29]. The L 2 regularization strategy selects 10 -6 weight decay, and the objective function is modified as equation (19).…”

Section: Ai and DLmentioning

confidence: 99%

Front-End Model of Wireless Network Combined with Artificial Intelligence in Computer Information Management System

Wang

2022

Wireless Communications and Mobile Computing

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To address the security problem of computer information management, an artificial intelligence- (AI-) based information intrusion detection model is built in combination with wireless network. Firstly, the background and characteristics of wireless local area network (WLAN) technology are analyzed, and the relationship between AI technology and deep learning is introduced. Secondly, an intrusion detection model on account of long short-term memory (LSTM) neural network and gated recursive unit (GRU) is constructed after analysis of different neural network models. The L2 weight attenuation and dropout regularization strategies are combined with the neural network model. Finally, an intrusion detection front-end model combining wireless network and AI is established. From the comparison of intrusion detection experiments, the generalization ability of the model can be improved by using L2 weight attenuation and dropout regularization strategies. Nevertheless, the performance improvement is only slight, so the early stop method is adopted instead of the regularization strategy. Compared with the existing classification models, the overall performance of LSTM and GRU models is improved by about 17%. The performance of GRU model is not much different from that of LSTM model, but the amount of computation is reduced. Therefore, GRU model is the optimal choice to construct intrusion detection system. The intrusion detection models in WLAN and GRU can improve the security performance of computer information management system. To sum up, this work provides reference for the development of computer management system.

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Section: Ai and DLmentioning

confidence: 99%

Front-End Model of Wireless Network Combined with Artificial Intelligence in Computer Information Management System

Wang

2022

Wireless Communications and Mobile Computing

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“…ISO/IEC 14443 consists of four parts: (1) physical characteristics, (2) RF power and signal interface, (3) initialization and anti-collision, and (4) transmission protocol. It also defines two kinds of tags (type A and type B), which differ in parts (2) and (3). Table 1 shows the differences in terms of modulation and coding between both types [in such a table, the reader is called proximity coupling device (PCD), and the tag is the proximity integrated circuit card (PICC)].…”

Section: Understanding the Underlying Protocolsmentioning

confidence: 99%

“…However, despite becoming an everyday technology, many public and private entities have not considered the security of RFID systems as a basic requirement. In fact, it is easy to find many commercial systems that contain critical security flaws and vulnerabilities [1,2] that allow for cloning tags or for straight signal replaying. Such vulnerabilities let attackers access certain services or facilities, get or alter personal information, and even track people.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for Evaluating Security in Commercial RFID Systems

Fernández‐Caramés¹,

Fraga‐Lamas²,

Albela³

et al. 2017

Radio Frequency Identification

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Although RFID has become a widespread technology, the developers of numerous commercial systems have not taken care of security properly. This chapter presents a methodology for detecting common security flaws. The methodology is put in practice using an open-source RFID platform (Proxmark 3), and it is tested in different fields, such as public transportation or animal identification. The results obtained show that the consistent application of the methodology allows researchers to perform security audits easily and detect, mitigate, or avoid risks and possible attacks.

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“…Most of the developed countries are running behind the RFID technology to strengthen their tracking accuracy. Currently, RFID systems are used for various tiny Tracking applications [3]- [6]. Some of the major real-time applications of RFID systems are the Internet of Things (IoT), Supply chain management, Vehicle tracking and location Identification of an object [7]- [11].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Large-Scale RFID Networks With Energy-Efficient Dynamic Cluster Head Selection: A Performance Improvement Approach

Pandian,

Somasundaram,

Sahu

et al. 2024

IEEE Access

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This paper focuses on the use of radio frequency signals for non-contact tracking and localization utilising Radio Frequency Identification (RFID) technology. The clustering approach is quite useful when dealing with large RFID readers. This method stimulates the expansion of RFID nodes without compromising the overall network performance. The Cluster Head (CH) is the most critical node in a clustered RFID system. This research presents a method for dynamically selecting the cluster head that takes the connectivity and power of each RFID reader into account. In dynamic mode, selecting a new cluster head is based on Fuzzy Logic. Based on these data, the energy level of 0.443 and centrality of 0.809 are the thresholds at which a node has a 91.8 % chance of becoming a CH. The management of massive RFID networks is also handled, with cluster numbers increasing at different time intervals. The RFID network's effectiveness is determined by measuring throughput, accuracy, delay, success, and error rates. The network may accommodate up to a thousand nodes with 13 node leaders for improved capacity. The results show a 97.8 % success rate, 0.22 % accuracy, a 2.64 % error rate, and a 36.91 second latency.

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RFID: An appraisal of malevolent attacks on RFID security system and its resurgence

Cited by 9 publications

References 12 publications

Front-End Model of Wireless Network Combined with Artificial Intelligence in Computer Information Management System

Front-End Model of Wireless Network Combined with Artificial Intelligence in Computer Information Management System

A Methodology for Evaluating Security in Commercial RFID Systems

Optimizing Large-Scale RFID Networks With Energy-Efficient Dynamic Cluster Head Selection: A Performance Improvement Approach

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