A secure and computationally efficient authentication and key agreement scheme for Internet of Vehicles

Xu, Zisang; Li, Xiong; Xu, Jianbo; Liang, Wei; Choo, Kim‐Kwang Raymond

doi:10.1016/j.compeleceng.2021.107409

Cited by 32 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The energy consumption needed for the proposed protocol is (896*.00066+6*.000217+2*.000108=0.59 mj). The proposed protocol uses the AES and hash function which requires lesser energy consumption as compared to RSA or ECC [28]. Therefore, we can infer from the output of energy consumption shown in Table V that proposed protocol consumes lesser energy not only the protocols [3], [14], [15], [19], [22], [23] that use the RSA and ECC but also the protocols [11]- [13] that use the AES and hash function.…”

Section: E Energy Consumptionmentioning

confidence: 93%

“…In this section, we compute the energy required for the proposed protocol and compare it with other related protocols. We compute the energy consumption as in [28]. The energy usage of a "StrongARM" CPU running at 133 MHz doing various tasks is summarised as the energy required for transmitting a bit is 0.00066 mj, energy required for AES symmetric enc/dec is 0.00217 mj, energy required for Hashed output is 0.000108 mj, energy required for public key enc/dec RSA is 15.3 mj.…”

Section: E Energy Consumptionmentioning

confidence: 99%

See 1 more Smart Citation

An EAP-Based Mutual Authentication Protocol for WLAN-Connected IoT Devices

Yadav

Misra

Pandey

et al. 2023

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

Several symmetric and asymmetric encryption based authentication protocols have been developed for the Wireless Local Area Networks (WLANs). However, recent findings reveal that these protocols are either vulnerable to numerous attacks or computationally expensive. Considering the demerits of these protocols and the necessity to provide enhanced security, a lightweight Extensible Authentication Protocol (EAP)-based authentication protocol for WLAN-connected IoT devices is presented. We conduct an informal and formal security analysis to ensure robustness against the attacks. Furthermore, the empirical performance analysis and comparison show that the proposed protocol outperforms its counterparts, reducing computational, communication, storage costs, and energy consumption by up to 99%, 80%, 91.8%, and 98%, respectively. Simulation results of the protocol using the NS3 and its overhead under unknown attacks demonstrate that the proposed protocol performs better in all scenarios. A prototype implementation of the protocol has also been tested to evaluate its feasibility in real-time applications.

show abstract

Section: E Energy Consumptionmentioning

confidence: 93%

Section: E Energy Consumptionmentioning

confidence: 99%

An EAP-Based Mutual Authentication Protocol for WLAN-Connected IoT Devices

Yadav

Misra

Pandey

et al. 2023

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

show abstract

“…The Alibaba Elastic Compute Service (ECS) has been used for simulating a smart home system in [25][26][27][28][29]. Edge computing technologies were used for the hardware architecture.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Home IoT Privacy and Security Preservation via Machine Learning燭echniques

Almutairi¹

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The development and use of Internet of Things (IoT) devices have grown significantly in recent years. Advanced IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT devices. Corporations have begun to embrace the IoT concept. Identifying true and suitable devices, security faults that might be used for bad reasons, and administration of such devices are only a few of the issues that IoT, a new concept in technological progress, provides. In some ways, IoT device traffic differs from regular device traffic. Devices with particular features can be classified into categories, irrespective of their function or performance. Ever-changing and complex environments, like a smart home, demand this classification scheme. A total of 41 IoT devices were employed in this investigation. To build a multiclass classification model, IoT devices contributed 13 network traffic parameters. To further preprocess the raw data received, preprocessing techniques like Normalization and Dataset Scaling were utilized. Feature engineering techniques can extract features from the text data. A total of 117,423 feature vectors are contained in the dataset after stratification, which are used to further improve the classification model. In this study, a variety of performance indicators were employed to show the performance of the logiboosted algorithms. Logi-XGB scored 80.2% accuracy following application of the logit-boosted algorithms to the dataset for classification, whereas Logi-GBC achieved 77.8% accuracy. Meanwhile, Logi-ABC attained 80.7% accuracy. Logi-CBC, on the other hand, received the highest Accuracy score of 85.6%. The accuracy of Logi-LGBM and Logi-HGBC was the same at 81.37% each. Our suggested Logi-CBC showed the highest accuracy on the dataset when compared to existing Logit-Boosted Algorithms used in earlier studies.

show abstract

“…Attribute-based encryption (ABE) is employed in this system to offer fine-grained access control and synonym keyword search. Reference [20] proposed a retrieval scheme for encrypted electronic medical records. The scheme uses a hierarchical attribute vector representation method to achieve flexible and complex multi-domain keyword join queries, such as subset queries and range query etc.…”

Section: Medical Information Retrievalmentioning

confidence: 99%

MEC-IoT-Healthcare: Analysis and Prospects

Wang¹,

Dauwed²,

Khan³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Physical sensors, intelligent sensors, and output recommendations are all examples of smart health technology that can be used to monitor patients' health and change their behavior. Smart health is an Internet-of-Things (IoT)-aware network and sensing infrastructure that provides real-time, intelligent, and ubiquitous healthcare services. Because of the rapid development of cloud computing, as well as related technologies such as fog computing, smart health research is progressively moving in the right direction. Cloud, fog computing, IoT sensors, blockchain, privacy and security, and other related technologies have been the focus of smart health research in recent years. At the moment, the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance, including cloud storage, retrieval, and calculation of health big data. This article reviews state-of-the-art edge computing methods that has shifted to the collection, transmission, and calculation of health data, which includes various sensors and wearable devices used to collect health data, various wireless sensor technologies, and how to process health data and improve edge performance, among other things. Finally, the typical smart health application cases, blockchain's application in smart health, and related privacy and security issues were reviewed, as well as future difficulties and potential for smart health services. The comparative analysis provides a reference for the the mobile edge computing in healthcare systems.

show abstract

A secure and computationally efficient authentication and key agreement scheme for Internet of Vehicles

Cited by 32 publications

References 17 publications

An EAP-Based Mutual Authentication Protocol for WLAN-Connected IoT Devices

An EAP-Based Mutual Authentication Protocol for WLAN-Connected IoT Devices

Smart Home IoT Privacy and Security Preservation via Machine Learning燭echniques

MEC-IoT-Healthcare: Analysis and Prospects

Contact Info

Product

Resources

About