Security in internet of things: a review on approaches based on blockchain, machine learning, cryptography, and quantum computing

Cherbal, Sarra; Zier, Abdelhak; Hebal, Sara; Louail, Lemia; Annane, Boubakeur

doi:10.1007/s11227-023-05616-2

Cited by 27 publications

(4 citation statements)

References 127 publications

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“…Because security is so important to the adoption of any new technology, it is a perceptual issue for researchers working in the Internet of Things. To handle a specific scenario of protecting an IoT network, a great deal of research has been done focusing on the degree of security available on a certain mechanism, on particular applications, or on classifying vulnerabilities [20].…”

Section: Literature Surveymentioning

confidence: 99%

Accurate and Efficient Security Authentication of IoT Devices using Machine Learning Algorithms

Alghamdi,

Alzahrani

2024

Networks, Blockchain and Internet of Things

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The rapid proliferation of Internet of Things (IoT) devices has led to an increase in botnet attacks targeting these devices. A botnet attack is a cyber-attack in which a network of compromised devices, referred to as "bots" or "zombies," is utilized to execute a synchronized attack. These attacks can result in substantial harm to both the devices and the network to which they are connected. This study investigates the deployment of security authentication protocols to verify the identity of IoT devices prior to network connection. The study also evaluates the classification accuracy of four distinct supervised machine learning algorithms: Random Forest (RF), Naïve Bayes (NB), DecisionTree (DT), and eXtreme Gradient Boosting (XGBoost). It was foundXGBoost was the best performing classifier among the various machine learning algorithms tested, in terms of detecting botnet attacks in IoT networks using the Bot-IoT dataset.

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Section: Literature Surveymentioning

confidence: 99%

Accurate and Efficient Security Authentication of IoT Devices using Machine Learning Algorithms

Alghamdi,

Alzahrani

2024

Networks, Blockchain and Internet of Things

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“…• Differential and Linear Cryptanalysis: These are analytical techniques that aim to discover patterns or relationships between the plaintext, ciphertext, and key. Attackers look for specific differentials or linear equations that may reveal key bits or reduce the security of the encryption (20,21) . • Side-Channel Attacks: These attacks exploit information that is inadvertently leaked during the cryptographic operation, such as power consumption, electromagnetic radiation, or timing.…”

Section: Attack Resistance Analysis Using Avalanche Effectmentioning

confidence: 99%

Attack Analysis on Hybrid-SIMON-SPECKey Lightweight Cryptographic Algorithm for IoT Applications

Neve,

Bansode

2024

IJST

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Objective: To perform attack analysis on new developed hybrid-SIMON-SPECKey lightweight cryptographic algorithms and compare its strength with existing SIMON and SPECK Lightweight cryptographic algorithm. Methods: A hybrid-SIMON-SPECKey algorithm is the combination of round function of SIMON and key scheduling of SPECK algorithm. Both SIOMN & SPECK algorithm are used for securing resource constrained devices. In this research work, avalanche effect method is used to analyze attack resistance property of algorithm. Findings: Newly developed Hybrid algorithm shows better results in terms of execution time and memory consumption. As compared to SIMON, hybrid version of algorithm consumes 50% less time and 20% less memory, which makes it efficient. Strict Avalanche criteria for SIMON is 89%, that of SPECK is 90% and in case of hybrid algorithm, it is 90% at start position but when the character is flipped or changed at the end position of plain text then SAC is more (87%) in case of hybrid algorithm as compared as SIMON and SPECK algorithms. Hence, newly developed algorithm showed improved results with equally resistance to the attack as compared to SIMON & SPECK. Novelty and applications: The novelty lies in the creation of a hybrid lightweight cryptographic algorithm that combines the feistel structure of SIMON with the key scheduling function of SPECK. This hybrid approach aims to leverage the strengths of both algorithms, potentially providing a more robust and efficient solution for resource-constrained IoT devices. In section 3.1 comparative analysis is done which show that hybrid algorithm outperforms in term of time and memory consumption as well a strength of newly developed hybrid algorithm is evaluated using avalanche effect which shows that it is at par with base algorithms. Keywords: Attack Analysis, Cipher Code, Decryption, Encryption, Lightweight Cryptography, Iot Devices, And Resource Constraint Devices

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“…A more recent study [34], examined the challenges and strategies related to cloud computing and IoT security. Using blockchain, machine learning, cryptography, and quantum computing as examples, authors in [35] reviewed potential solutions for securing IoT by comparative analysis of the related papers. Then, they classified the relevant solutions based on their stated requirements for security.…”

Section: Inclusionmentioning

confidence: 99%

“…[34] Major difficulties with cloud computing, IoT, and Cloud-IoT security and strategies are discussed. [35] Emerging and traditional approaches to IoT security are discussed, including blockchain, machine learning, cryptography, and quantum computing. [36] Several articles are reviewed and various attacks and security challenges are discussed, including blockchain-based approaches.…”

Section: Inclusionmentioning

confidence: 99%

IoT Security Model for Smart Cities based on a Metamodeling Approach

Alotaibe

2024

Eng. Technol. Appl. Sci. Res.

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Security solutions for the Internet of Things (IoT) in smart cities are complex and require a comprehensive approach to success. Several models and frameworks have been developed focusing on IoT security. Some deal with access controls and security and some with authentication and authorization in various forms. Literature still lacks a comprehensive IoT security model for smart cities, which can support the implementation of IoT. Accordingly, this study has set two objectives: to explore the present studies in IoT security for smart cities and to develop an IoT security model for smart cities based on the metamodeling approach. According to the findings of the study, the existing IoT security models for smart cities consider seven security aspects: authentication and authorization, device management, intrusion detection and prevention, device integrity, secure communication, secure data storage, and response to security incidents. The model developed in this study, called IoT Security Metamodel (IoTSM), combines these aspects. IoTSM captures the main qualities of IoT security practices in smart cities through domain security processes.

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Security in internet of things: a review on approaches based on blockchain, machine learning, cryptography, and quantum computing

Cited by 27 publications

References 127 publications

Accurate and Efficient Security Authentication of IoT Devices using Machine Learning Algorithms

Accurate and Efficient Security Authentication of IoT Devices using Machine Learning Algorithms

Attack Analysis on Hybrid-SIMON-SPECKey Lightweight Cryptographic Algorithm for IoT Applications

IoT Security Model for Smart Cities based on a Metamodeling Approach

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