An Efficient Lightweight Cryptographic Algorithm for IoT Security

AbdulRaheem, Muyideen; Awotunde, Joseph Bamidele; Jimoh, Rasheed Gbenga; Oladipo, Idowu Dauda

doi:10.1007/978-3-030-69143-1_34

Cited by 22 publications

(7 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was observed that the RSA algorithm generated large files and consumed more space (memory) during the encryption process of all categories of text, audio, image, and video data concerning the CPU's internal memory usage. This is consistent with the existing literature (Meshram et al, 2022;Abdulraheem et al, 2021a;Oladipupo et al, 2023). ElGamal algorithm outperformed the RSA algorithm in terms of memory usage.…”

Section: Discussionsupporting

confidence: 91%

“…It is a measure of how 'good' the algorithm is at solving the problem. It can also be described as the efficiency of the algorithm in terms of the amount of data the algorithm must process (Abdulraheem et al, 2021a). Typically, the complexity of an algorithm is a function that maps the input length/size to the number of main stages (time complexity) or specific storage positions (space complexity).…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Abdulraheem et al (2021b) suggested a simple, provably secure certificateless method for group oriented signcryption (CGST) using Fractional Chaotic Maps (FCM). Any group signcrypter may encrypt data or information with the Group Manager (GM) and have it delivered to the verifier without interruption using the CGST-FCM protocol.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Analysis of Two Famous Cryptographic Algorithms on Mixed Data

Adeniyi¹,

Imoize²,

Awotunde³

et al. 2023

Journal of Computer Science

View full text Add to dashboard Cite

The rapid development of digital data sharing has made information security a crucial concern in data communication. The information security system heavily relies on encryption methods. These algorithms employ strategies to increase data secrecy and privacy by obscuring the information, which only those parties who have the accompanying key can decode or decrypt. Nevertheless, these methods also use a lot of computational resources, including battery life, memory, and CPU time. So, to determine the optimal algorithm to utilize moving forward, it is necessary to assess the performance of various cryptographic algorithms. Therefore, this study evaluates two well-known cryptographies (RSA and ElGamal) using mixed data such as binary, text, and image files. CPU internal clock was used to obtain the time complexity used by both algorithms during encryption and decryption. The algorithms used CPU internal memory to obtain memory usage during the encryption and decryption of mixed data. Evaluation criteria such as encryption time, decryption time, and throughput were used to compare these encryption algorithms. The response time, confidentiality, bandwidth, and integrity are all factors in the cryptography approach. The results revealed that RSA is a time-efficient and resourceful model, while the ElGamal algorithm is a memory-efficient and resourceful model.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Analysis of Two Famous Cryptographic Algorithms on Mixed Data

Adeniyi¹,

Imoize²,

Awotunde³

et al. 2023

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…Machine-to-machine (M2M) and machine-to-person (M2P) connections to the network are used in IIoTs using the TCP/IP interface using various IIoT protocols [9,10]. The number IIoTs have the number of flaws and bugs that can be abused using a range of advanced attack methods which has increased significantly.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection

Awotunde

Chakraborty

Adeniyi

2021

Wireless Communications and Mobile Computing

Self Cite

108

View full text Add to dashboard Cite

The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning-based intrusion detection paradigm for IIoT with hybrid rule-based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule-based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well-known network datasets, NSL-KDD and UNSW-NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL-KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW-NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.

show abstract

“…In 2021, the authors in [23] proposes a new modification of TEA for securing data in IoT devices. The proposed algorithm is based on block ciphers and hash functions, which enables efficient encryption and decryption of data while also ensuring data integrity.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancement of Tiny Encryption Algorithm for Resource-Constrained WSNs Using Four Connected Additive Fibonacci Generators

Zyara,

Mhaibes,

Shallal

2024

JCSANDM

View full text Add to dashboard Cite

The usage of wireless sensor networks (WSN) is widespread in industries where data security is crucial. Due to the energy and computational limits of WSN, the cryptographic protocols designed for it must be as computationally cheap as feasible. The components of these protocols, such as the random number generator, are subject to the same constraints. For such resource-constrained devices, several lightweight encryption techniques have been created. One of the most efficient lightweight ciphers is Tiny Encryption Algorithm (TEA). TEA uses a few lines of source code that are based on Feistel. It is however susceptible to attacks utilizing equivalent and related key attacks. In order to address TEA’s key vulnerabilities, a modification is suggested in this paper that focuses on key creation. Four connected Additive Fibonacci Generators (AFGs) make up the structure, which addresses the security vulnerability by using a unique key each round. Performance evaluation was assessed using three statistical tests: avalanche effect, randomness analysis, and completeness testing. Through experimental results, ATEA outperforms TEA by an average of 51.68 % to 47.51 % for the avalanche effect, and 51.95 % to 48.36 % for the completeness test, and satisfies all the NIST requirements. Results of advanced security measurements indicate that, ATEA can be used to secure WSN devices.

show abstract

An Efficient Lightweight Cryptographic Algorithm for IoT Security

Cited by 22 publications

References 29 publications

Performance Analysis of Two Famous Cryptographic Algorithms on Mixed Data

Performance Analysis of Two Famous Cryptographic Algorithms on Mixed Data

Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection

Enhancement of Tiny Encryption Algorithm for Resource-Constrained WSNs Using Four Connected Additive Fibonacci Generators

Contact Info

Product

Resources

About