Security Analysis of LNMNT-LightWeight Crypto Hash Function for IoT

Nabeel, Nubila; Habaebi, Mohamed Hadi; Islam, Md. Rafiqul

doi:10.1109/access.2021.3133097

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…The experimental outcomes indicated that the projected method was effective for mitigating the number of packets up to 39% and detecting malevolent packets at an accuracy up to 99.99% in comparison with the traditional methods. N. Nabeel, et.al (2021) constructed a novel Lightweight (LWT) hash function known as Lightweight New Mersenne Number Transform (LNMNT) to secure diverse Internet of Things (IoT) applications [22]. The arbitrariness, confusion, diffusion, distributed hash function, and dissimilar attacks were considered for quantifying the constructed approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Internet of Things Security Through Optimization Algorithms and Machine Learning

Zakiya Manzoor Khan

2024

cana

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IoT network traffic classification is an approach used to analyze IoT network traffic, revealing various network activities. The network traffic analysis process involves several steps: data input, preprocessing, feature extraction, classification, and performance analysis. Although various machine learning algorithms have been proposed in recent years, they have failed to achieve high accuracy and effectively extract features from datasets. This research aims to develop an algorithm that can extract features and achieve high accuracy in network traffic classification.To accomplish this, a hybrid optimization algorithm combining genetic algorithms and Particle Swarm Optimization (PSO) is proposed. This hybrid algorithm extracts features, which are then classified using Random Forest. The proposed model is implemented in Python, and its performance is evaluated in terms of accuracy, precision, and recall.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Internet of Things Security Through Optimization Algorithms and Machine Learning

Zakiya Manzoor Khan

2024

cana

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“…The basic principle is to control an object or entity's inherent random nature and facilitate the realization of PUF expression. Due to strong randomness, PUF generates a set of unique keys that is applicable to IoT applications [52]. Cryptographic functions use biometric data inputs for extracting the fuzzy functions [53] utilized for encryption, authentication, and decryption.…”

Section: Background Of Puf Schemesmentioning

confidence: 99%

Enhancing IoT Security through a Green and Sustainable Federated Learning Platform: Leveraging Efficient Encryption and the Quondam Signature Algorithm

Aljrees,

Kumar,

Singh

et al. 2023

Sensors

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This research paper introduces a novel paradigm that synergizes innovative algorithms, namely efficient data encryption, the Quondam Signature Algorithm (QSA), and federated learning, to effectively counteract random attacks targeting Internet of Things (IoT) systems. The incorporation of federated learning not only fosters continuous learning but also upholds data privacy, bolsters security measures, and provides a robust defence mechanism against evolving threats. The Quondam Signature Algorithm (QSA) emerges as a formidable solution, adept at mitigating vulnerabilities linked to man-in-the-middle attacks. Remarkably, the QSA algorithm achieves noteworthy cost savings in IoT communication by optimizing communication bit requirements. By seamlessly integrating federated learning, IoT systems attain the ability to harmoniously aggregate and analyse data from an array of devices while zealously guarding data privacy. The decentralized approach of federated learning orchestrates local machine-learning model training on individual devices, subsequently amalgamating these models into a global one. Such a mechanism not only nurtures data privacy but also empowers the system to harness diverse data sources, enhancing its analytical capabilities. A thorough comparative analysis scrutinizes varied cost-in-communication schemes, meticulously weighing both encryption and federated learning facets. The proposed approach shines by virtue of its optimization of time complexity through the synergy of offline phase computations and online phase signature generation, hinged on an elliptic curve digital signature algorithm-based online/offline scheme. In contrast, the Slow Block Move (SBM) scheme lags behind, necessitating over 25 rounds, 1500 signature generations, and an equal number of verifications. The proposed scheme, fortified by its marriage of federated learning and efficient encryption techniques, emerges as an embodiment of improved efficiency and reduced communication costs. The culmination of this research underscores the intrinsic benefits of the proposed approach: marked reduction in communication costs, elevated analytical prowess, and heightened resilience against the spectrum of attacks that IoT systems confront.

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“…LNMNT Hash is based on the new Mersenne number transform (NMNT). The designer provided a security analysis in [81]. The designer analyzed the randomness, obfuscation, diffusion, hash value distribution, and differential attacks.…”

Section: Sponge Constructionmentioning

confidence: 99%

Lightweight Cryptographic Hash Functions: Design Trends, Comparative Study, and Future Directions

et al. 2022

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The emergence of the Internet of Things (IoT) has enabled billions of devices that collect large amounts of data to be connected. Therefore, IoT security has fundamental requirements. One critical aspect of IoT security is data integrity. Cryptographic hash functions are cryptographic primitives that provide data integrity services. However, due to the limitations of IoT devices, existing cryptographic hash functions are not suitable for all IoT environments. As a result, researchers have proposed various lightweight cryptographic hash function algorithms. In this paper, we discuss advanced lightweight cryptographic hash functions for highly constrained devices, categorize design trends, analyze cryptographic aspects and cryptanalytic attacks, and present a comparative analysis of different hardware and software implementations. In the final section of this paper, we highlight present research challenges and suggest future research topics related to the design of lightweight cryptographic hash functions.

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Security Analysis of LNMNT-LightWeight Crypto Hash Function for IoT

Cited by 10 publications

References 27 publications

Enhancing Internet of Things Security Through Optimization Algorithms and Machine Learning

Enhancing Internet of Things Security Through Optimization Algorithms and Machine Learning

Enhancing IoT Security through a Green and Sustainable Federated Learning Platform: Leveraging Efficient Encryption and the Quondam Signature Algorithm

Lightweight Cryptographic Hash Functions: Design Trends, Comparative Study, and Future Directions

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