Edge Data Security for RFID-based Devices

Ambareen, Javeria; Prabhakar, M.; Ara, Tabassum

doi:10.1109/icstcee49637.2020.9277007

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…(Abbas,et al,2020).IoT devicesareusedfortheagriculturalenvironmentforagriculturalsecuritysurveillance,cropirrigation efficiency,andfooddatastorage. (Saigopal,et al,2020).Indistributionnetworksectors,thefreight anddistributionsectoriscritical(Chaurasia2020; Maharani,et al,2020;Ambareen,et al,2020;Juárez 2019) (Zolanvari,et al,2021;Priya,et al,2021).Asaresult,tocombatassaultsandsecureindustrialsystems,acomprehensiveintrusion detectiontechniqueisnecessary. Themajorcontributionofthisresearchworkis:…”

Section: Industrial Iot (Iiot) Network: Ids Requirementmentioning

confidence: 99%

Deep Ensemble Model for Detecting Attacks in Industrial IoT

Behera¹,

Pattanayak²,

Mohanty

2022

International Journal of Information Security and Privacy

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In this research work, a novel IIoT attack detection framework is designed by following four major phases: pre-processing, imbalance processing, feature extraction, and attack detection. The attack detection is carried out using the projected ensemble classification framework. The projected ensemble classification framework encapsulates the recurrent neural network, CNN, and optimized bi-directional long short-term memory (BI-LSTM). The RNN and CNN in the ensemble classification framework is trained with the extracted features. The outcome acquired from RNN and CNN is utilized for training the optimized BI-LSTM model. The final outcome regarding the presence/absence of attacks in the industrial IoT is portrayed by the optimized BI-LSTM model. Therefore, the weight of BI-LSTM model is fine-tuned using the newly projected hybrid optimization model referred as cat mouse updated slime mould algorithm (CMUSMA). The projected hybrids the concepts of both the standard slime mould algorithm (SMA) and cat and mouse-based optimizer(CMBO), respectively.

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Section: Industrial Iot (Iiot) Network: Ids Requirementmentioning

confidence: 99%

Deep Ensemble Model for Detecting Attacks in Industrial IoT

Behera¹,

Pattanayak²,

Mohanty

2022

International Journal of Information Security and Privacy

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“…An encryption protocol that combines the advanced encryption standard (AES) and elliptic curve cryptography (ECC) algorithms to enhance RFID data security is proposed in paper [9], this approach aims to achieve optimal effectiveness and efficiency in safeguarding the information. In [10], data security is ensured by using peak modulation and backscattering to simultaneously modulate sensor and identification (ID) information. In [11], the authors have developed an algorithm that effectively secures the information stored in RFID tags through the use of triple DES and RSA encryption.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a cryptographic algorithm based on the AES advanced encryption standard for UHF RFID systems

Habibi,

Sahel,

Bendali

et al. 2024

TELKOMNIKA

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In this paper, a proposal is made for a cryptographic algorithm designed for passive ultra-high-frequency (UHF) radio frequency identification systems. The algorithm relies on the advanced encryption standard (AES) as its fundamental encryption technique, augmented by two supplementary steps: the initial step involves generating a random key and the second is the randomization of data, this introduces an extra level of security to encryption process against attacks. The developed architecture has been optimized to minimize hardware resource consumption with faster execution speed. The algorithm has been simulated, synthesized and implemented in an xtreme digital signal processing (DSP) starter kit equipped with xilinx's spartan-3A DSP 1800A edition and it serves the purpose of encrypting and decrypting user data on a radio frequency identification (RFID) passive tag. The main objective is to make it difficult to break the algorithm because of its multiple steps. The experimental results showed that the speed, functionality and cost of encryption and decryption make this a perfectly practical solution, providing a satisfactory level of security for today's communications systems, or other electronic data transfer processes where security is required.

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Edge Data Security for RFID-based Devices

Cited by 2 publications

References 6 publications

Deep Ensemble Model for Detecting Attacks in Industrial IoT

Deep Ensemble Model for Detecting Attacks in Industrial IoT

Design and implementation of a cryptographic algorithm based on the AES advanced encryption standard for UHF RFID systems

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