MUMAP: Modified Ultralightweight Mutual Authentication protocol for RFID enabled IoT networks

Article


Raju, M. H., Ahmed, M. U. and Ahad, M. A. R. 2021. MUMAP: Modified Ultralightweight Mutual Authentication protocol for RFID enabled IoT networks. Journal of the Institute of Industrial Applications Engineers. 9 (2), pp. 33-39. https://doi.org/10.12792/JIIAE.9.33
AuthorsRaju, M. H., Ahmed, M. U. and Ahad, M. A. R.
Abstract

Flawed authentication protocols led to the need for a secured protocol for radio frequency identification (RFID) techniques. In this paper, an authentication protocol named Modified ultralightweight mutual authentication protocol (MUMAP) has been proposed and cryptanalysed by Juel-Weis challenge. The proposed protocol aimed to reduce memory requirements in the authentication process for low-cost RFID tags with limited resources. Lightweight operations like XOR and Left Rotation, are used to circumvent the flaws made in the other protocols. The proposed protocol has three-phase of authentication. Security analysis of the proposed protocol proves its resistivity against attacks like desynchronization, disclosure, tracking, and replay attack. On the other hand, performance analysis indicates that it is an effective protocol to use in low-cost RFID tags. Juel-Weis challenge verifies the proposed protocol where it shows insusceptibility against modular operations.

KeywordsSecurity; RFI; Mutual authentication protocol
JournalJournal of the Institute of Industrial Applications Engineers
Journal citation9 (2), pp. 33-39
ISSN2187-8811
Year2021
PublisherThe Institute of Industrial Applications Engineers (IIAE)
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.12792/JIIAE.9.33
Publication dates
Online19 Apr 2021
Publication process dates
Deposited04 Dec 2023
Copyright holder© 2021, The Authors
Permalink -

https://repository.uel.ac.uk/item/8wz2x

Download files


Publisher's version
285-Article Text-2190-4-10-20210420.pdf
License: CC BY 4.0
File access level: Anyone

  • 30
    total views
  • 9
    total downloads
  • 3
    views this month
  • 1
    downloads this month

Export as

Related outputs

Learn Programming with C: An Easy Step-by-Step Self-Practice Book for Learning C
Imran, S. M. S. and Ahad, M. A. R. 2024. Learn Programming with C: An Easy Step-by-Step Self-Practice Book for Learning C. CRC Press: Taylor & Francis Group.
Deep learning with image-based autism spectrum disorder analysis: A systematic review
Uddin, M. Z., Shahriar, M. A., Mahamood, M. N., Alnajjar, F., Pramanik, M. I. and Ahad, M. A. R. 2024. Deep learning with image-based autism spectrum disorder analysis: A systematic review. Engineering Applications of Artificial Intelligence. 127 (Art. 107185). https://doi.org/doi.org/10.1016/j.engappai.2023.107185
Annotator-dependent uncertainty-aware estimation of gait relative attributes
Shehata, A., Makihara, Y., Muramatsu, D., Ahad, M. and Yasushi, Y. 2023. Annotator-dependent uncertainty-aware estimation of gait relative attributes. Pattern Recognition. 136 (Art. 109197). https://doi.org/10.1016/j.patcog.2022.109197
HID 2022: The 3rd International Competition on Human Identification at a Distance
Yu, S., Huang, Y., Wang, L., Makihara, Y., Wang, S., Ahad, M. and Nixon, M. 2022. HID 2022: The 3rd International Competition on Human Identification at a Distance. IJCB 2022: IEEE International Joint Conference on Biometrics. Abu Dhabi, UAE 10 - 13 Dec 2023 IEEE. https://doi.org/10.1109/IJCB54206.2022.10007993
Automated detection approaches to autism spectrum disorder based on human activity analysis: A review
Rahman, S., Ahmed, S. F., Shahid, O., Arrafi, M. A. and Ahad, M. A. R. 2022. Automated detection approaches to autism spectrum disorder based on human activity analysis: A review. Cognitive Computation. 14, pp. 1773-1800. https://doi.org/10.1007/s12559-021-09895-w
A Sleep Monitoring System Using Ultrasonic Sensors
Shammi, U. A. and Ahad, M. 2022. A Sleep Monitoring System Using Ultrasonic Sensors. International Journal of Biomedical Soft Computing and Human Sciences. 27 (1), pp. 13-20. https://doi.org/10.24466/ijbschs.27.1_13
Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?
Nazmus Sakib, A. H. M., Basak, P., Doha Uddin, S., Mustavi Tasin, S. and Ahad, M. 2022. Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity? 3rd International Conference on Activity and Behavior Computing (ABC 2021). Online 22 - 23 Oct 2021 Springer Singapore. https://doi.org/10.1007/978-981-19-0361-8_10
Identification of Food Packaging Activity Using MoCap Sensor Data
Anwar, A., Islam Tapotee, M., Saha, P. and Ahad, M. 2022. Identification of Food Packaging Activity Using MoCap Sensor Data. 3rd International Conference on Activity and Behavior Computing (ABC 2021). Online 22 - 23 Oct 2021 Springer Singapore. https://doi.org/10.1007/978-981-19-0361-8_11
Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features
Pritom, Y. A., Rahman, M. S., Rahman, H. R., Kowshik, M. A. and Ahad, M. 2022. Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features. 3rd International Conference on Activity and Behavior Computing (ABC 2021). Online 22 - 23 Oct 2021 Springer Singapore. https://doi.org/10.1007/978-981-19-0361-8_12
Bento Packaging Activity Recognition Based on Statistical Features
Rakib Sayem, F., Sheikh, M. M. and Ahad, M. 2022. Bento Packaging Activity Recognition Based on Statistical Features. 3rd International Conference on Activity and Behavior Computing (ABC 2021). Online 22 - 23 Oct 2021 Springer Singapore. https://doi.org/10.1007/978-981-19-0361-8_13
Emotion Recognition from EEG Signal Focusing on Deep Learning and Shallow Learning Techniques
Islam, M. R., Moni, M. A., Islam, M. M., Rashed-Al-Mahfuz, M., Islam, M. S., Hasan, M. K., Hossain, M. S., Ahmad, M., Uddin, S., Azad, A., Alyami, S. A., Ahad, M. A. R. and Lió, P. 2021. Emotion Recognition from EEG Signal Focusing on Deep Learning and Shallow Learning Techniques. IEEE Access. 9, pp. 94601-94624. https://doi.org/10.1109/ACCESS.2021.3091487
Static Postural Transition-based Technique and Efficient Feature Extraction for Sensor-based Activity Recognition
Ahmed, M., Das Antar, A. and Ahad, M. 2021. Static Postural Transition-based Technique and Efficient Feature Extraction for Sensor-based Activity Recognition. Pattern Recognition Letters. 147, pp. 25-33. https://doi.org/10.1016/j.patrec.2021.04.001
Activity Recognition from Accelerometer Data Based on Supervised Learning for Wireless Sensor Network
Israt, F. A., Hossain, T., Inoue, S. and Ahad, M. A. R. 2021. Activity Recognition from Accelerometer Data Based on Supervised Learning for Wireless Sensor Network. International Journal of Biomedical Soft Computing and Human Sciences. 26 (2), pp. 73-86. https://doi.org/10.24466/ijbschs.26.2_73
Action recognition using Kinematics Posture Feature on 3D skeleton joint locations
Ahad, M. A. R., Ahmed, M., Antar, A. D., Makihara, Y. and Yagi. Y. 2021. Action recognition using Kinematics Posture Feature on 3D skeleton joint locations. Pattern Recognition Letters. 145, pp. 216-224. https://doi.org/10.1016/j.patrec.2021.02.013
Exploring Human Activities Using eSense Earable Device
Islam, M. S., Hossain, T., Ahad, M. and Inoue, S. 2021. Exploring Human Activities Using eSense Earable Device. in: Ahad, M., Inoue, S., Roggen, D. and Fujinami, K. (ed.) Activity and Behavior Computing Springer Singapore. pp. 169–185
Contactless Human Monitoring: Challenges and Future Direction
Mahbub, U., Rahman, T. and Ahad, M. 2021. Contactless Human Monitoring: Challenges and Future Direction. in: Ahad, M., Mahbub, U. and Ahad, M. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 335-364
Contactless Human Emotion Analysis Across Different Modalities
Nahid, N., Rahman, A. and Ahad, M. 2021. Contactless Human Emotion Analysis Across Different Modalities. in: Ahad, M., Mahbub, U. and Rahman, T. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 237-269
Contactless Fall Detection for the Elderly
Nahian, M. J. A., Raju, M. H., Tasnim, Z., Mahmud, M., Ahad, M. and Kaiser, M. S. 2021. Contactless Fall Detection for the Elderly. in: Ahad, M., Mahbub, U. and Rahman, T. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 203-235
Signal Processing for Contactless Monitoring
Billah, M. S., Ahad, M. and Mahbub, U. 2021. Signal Processing for Contactless Monitoring. in: Ahad, M., Mahbub, U. and Rahman, T. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 113-144
Skeleton-Based Activity Recognition: Preprocessing and Approaches
Sarker, S., Rahman, S., Hossain, T., Faiza Ahmed, S., Jamal, L. and Ahad, M. 2021. Skeleton-Based Activity Recognition: Preprocessing and Approaches. in: Ahad, M., Mahbub, U. and Rahman, T. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 48-81
IoT Sensor-Based Activity Recognition: Human Activity Recognition
Ahad, M., Antar, A. D. and Ahmed, M. 2021. IoT Sensor-Based Activity Recognition: Human Activity Recognition. Springer, Cham.
A Method for Sensor-Based Activity Recognition in Missing Data Scenario
Hossain, T., Ahad, M. A. R. and Inoue, S. 2020. A Method for Sensor-Based Activity Recognition in Missing Data Scenario. Sensors. 20 (14), pp. 1-23. https://doi.org/10.3390/s20143811
An AI-based Visual Aid with Integrated Reading Assistant for the Completely Blind
Khan, M. A., Paul, P., Rashid, M., Hossain, M. and Ahad, M. 2020. An AI-based Visual Aid with Integrated Reading Assistant for the Completely Blind. IEEE Transactions on Human-Machine Systems. 50 (6), pp. 507-517. https://doi.org/10.1109/THMS.2020.3027534