Object Authentication in the Context of the Internet of Things: A Survey

Saadeh, Maha; Sleit, Azzam; Sabri, Khair Eddin; Almobaideen, Wesam

doi:10.13052/jcsm2245-1439.932

Cited by 3 publications

(3 citation statements)

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“…A comparison between these techniques according to the used evaluation models and their security analysis is outlined. Saadeh et al in [61] targeted object authentication over IoT by presenting a comprehensive survey of schemes that addressed this domain. The aim is to provide guidance for future researchers in how to grab the details of such authentication schemes especially in the context of IoT.…”

Section: Related Workmentioning

confidence: 99%

“…Different authentication schemes have been proposed for wireless mobile communication and wireless sensor networks [3]. The significant requirements for a feasible authentication scheme development are: Lightweight algorithms and protocols for security, key management and distribution, mutual authentication, and using certificateless authentication that depends on cryptographic hash functions to reduce energy consumption and computation cost in order to be suitable to constrained devices [61]. The main contributions of this paper involve:…”

Section: Introductionmentioning

confidence: 99%

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Anonymous Authentication Protocols for Iot Based Healthcare: A Survey

Asassfeh

Obeid

Almobaideen

2022

Int. j. commun. netw. inf. secur.

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- Nowadays, with the increase in chronic diseases and the aging population in all countries, it has become a huge burden on hospitals to accommodate all patients and monitor them. Applying wireless sensors network for the IoT based medical system enabled medical doctors and families to monitor patients' conditions all the time through the collected data from sensors connected to the patients. These sensitive data should be transmitted through secure channels to hospitals or medical centers. Many approaches proposed to present solutions to the security challenges of the IoT based medical systems. In this paper we review the most prominent approaches that tackle the security and Anonymous Authentication (AA)in healthcare systems. Furthermore, we compare between these approaches in term of types of security attacks, security measures, the approaches that were used to solve some of the security issues, and the network technology used such as WSN and RFID. We found that every approach has some drawbacks regarding security attacks and security measures. We believe that some of security attacks like denial of service and modification attacks need to be considered in future research. The same goes for security measures like non-traceability, and backward and forward secrecy. Moreover, 80%of authentication schemes use certificateless authentication. This type of authentication has low computation cost and saves energy which is convenient to the constrained devices. AVISPA and Ban logic are the most common tools used for validation in the surveyed approaches. A comparison between these techniques according to different features is illustrated which may help the researchers to easily identify the gaps in the surveyed approaches so as to propose solutions for these issues

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anonymous Authentication Protocols for Iot Based Healthcare: A Survey

Asassfeh

Obeid

Almobaideen

2022

Int. j. commun. netw. inf. secur.

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“…As the number of attacks is increasing, more research is conducted to increase the security of IoT network and devices. For example, some authentication protocols are proposed by taking into consideration the limitation of resources in IoT devices [4] [5]. Others, propose different architectures design to increase the security such as [6] [7].…”

Section: Introductionmentioning

confidence: 99%

A Secure IoT Framework Based on Blockchain and Machine Learning

Shahin¹,

Sabri²

2022

IJCDS

Self Cite

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Internet of Things (IoT) network consists of many devices that communicate together and exchange data. IoT network has many applications especially in smart city and smart campus. IoT devices usually produce a huge amount of data that are stored in the cloud to be analyzed later. Data in general and IoT devices data in particular suffer from major security issues such as the availability and the integrity of data. Blockchain is a new technology that offers an interesting solution for the security of sensitive IoT data by protecting data against malicious tampering. However, the data stored in the blockchain cannot be altered, therefore, it should be validated before being stored in the blockchain especially that IoT devices are vulnerable to attacks. Machine learning algorithms are very useful to detect compromised IoT devices to ensure that only reliable data are stored in the blockchain.The main aim of this paper is to propose a framework for storing reliable IoT data in a secure way that preserves the integrity and availability properties. First, to detect compromised IoT devices, several machine learning algorithms are evaluated. The IoTID20 dataset for anomalous activity detection in IoT network is used to build our machine learning model. IoTID20 dataset contains 80 network features and 625783 records. For our experiment, 4000 records are selected randomly from the dataset. Two algorithms are used to select features namely Logistic Regression and Pearson's correlation. A total of 15 features are selected in order to classify packets as (Normal, Anomaly). Several machine learning algorithms are trained: Logistic Regression, Random Forest, Decision Tree, K Nearest Neighbors, AdaBoost, and Naïve Bayes. These algorithms are compared based on the following measurements: accuracy, precision, recall, F1 score, and the time for classification. The results reveal that Random Forest and AdaBoost classifiers give very close results and are considered the best classifiers based on all performance metrics used in the paper except the time. Regarding the time required to detect malicious records, Decision Tree and Naïve Bayes are the best as the time required to predict 4000 records is 0.015 seconds.A private blockchain is built to store normal data received from IoT devices after being filtered. The data is signed and then distributed to all nodes in the network for verification. To provide scalability, the consensus algorithm of the blockchain is based on the proof of authority algorithm. As a proof of concepts, the blockchain is developed using Python programming language and the time required to verify the signatures is computed.

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