An energy efficient data fault prediction based clustering and routing protocol using hybrid ASSO with MERNN in wireless sensor network

Mahalakshmi, G.; Ramalingam, S.; Manikandan, A.

doi:10.1007/s11235-024-01109-6

Cited by 18 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The need for robust authentication mechanisms in IoT environments to counter de-authentication attacks and unauthorized access is addressed. SecureKey leverages blockchain's decentralized and tamper-resistant nature to establish a secure and trustless authentication process for IoT devices [21]. The framework employs smart contracts to enforce authentication rules and facilitate secure interactions between devices, ensuring the integrity of communication within the IoT ecosystem.…”

Section: Related Workmentioning

confidence: 99%

Enhancing IoT Security: A Blockchain-Based Mitigation Framework for Deauthentication Attacks

Gopalan,

Manikandan,

Dharani

et al. 2024

Int J Netw Distrib Comput

Self Cite

View full text Add to dashboard Cite

The proposed Blockchain-Based Mitigation of Deauthentication Attacks (BBMDA) Framework aims to enhance the security and trustworthiness of IoT environments by leveraging blockchain technology, the Elliptic Curve Digital Signature Algorithm (ECDSA) for secure authentication, and Multi-Task Transformer (MTT) for efficient traffic classification. This paper presents a novel approach to mitigate de-authentication attacks in IoT ecosystems. The research methodology involves developing and implementing the BBMDA framework, followed by a comprehensive evaluation and comparison with existing techniques. Key findings indicate that the BBMDA framework outperforms traditional methods such as Support Vector Machine (SVM), k-nearest Neighbors (KNN), and Convolutional Neural Network (CNN) in terms of accuracy, false positive rate, false negative rate, precision, recall, and F1-score. These results underscore the effectiveness and efficiency of the proposed framework in enhancing IoT security.

show abstract

Section: Related Workmentioning

confidence: 99%