Optimization Scheme of Collaborative Intrusion Detection System Based on Blockchain Technology

Electronics, Journal Year: 2025, Volume and Issue: 14(2), P. 261 - 261

Published: Jan. 10, 2025

In light of the escalating complexity cyber threat environment, role Collaborative Intrusion Detection Systems (CIDSs) in reinforcing contemporary cybersecurity defenses is becoming ever more critical. This paper presents a Blockchain-based Framework (BCIDF), an innovative methodology aimed at enhancing efficacy detection and information dissemination. To address issue alert collisions during data exchange, Alternating Random Assignment Selection Mechanism (ARASM) proposed. mechanism aims to optimize selection process domain leader nodes, thereby partitioning traffic reducing size conflict domains. Unlike conventional CIDS approaches that typically rely on independent node-level detection, our framework incorporates Weighted Forest (WRF) ensemble learning algorithm, enabling collaborative among nodes significantly boosting system’s overall capability. The viability BCIDF has been rigorously assessed through extensive experimentation utilizing NSL-KDD dataset. empirical findings indicate outperforms traditional intrusion systems terms precision, offering robust highly effective solution within realm cybersecurity.

Language: Английский

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Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models

Ad Hoc Networks, Journal Year: 2025, Volume and Issue: 170, P. 103770 - 103770

Published: Jan. 27, 2025

Language: Английский

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Implementation of a Data-Parallel Approach on a Lightweight Hash Function for IoT Devices

Mathematics, Journal Year: 2025, Volume and Issue: 13(5), P. 734 - 734

Published: Feb. 24, 2025

The Internet of Things is used in many application areas our daily lives. Ensuring the security valuable data transmitted over a crucial challenge. Hash functions are cryptographic applications such as integrity, authentication and digital signatures. Existing lightweight hash leverage task parallelism but provide limited scalability. There need for algorithms that can efficiently utilize multi-core platforms or distributed computing environments with high degrees parallelization. For this purpose, data-parallel approach applied to function achieve massively parallel software. A novel structure suitable architectures, inspired by basic tree construction, designed. Furthermore, proposed based on block cipher seamlessly integrated into designed framework. satisfies requirements, exhibits efficiency achieves significant parallelism. Experimental results indicate performs comparably BLAKE implementation, slightly slower execution large message sizes marginally better performance smaller ones. Notably, it surpasses all other evaluated at least 20%, maintaining consistent 20% advantage Grostl across sizes. Regarding parallelism, PLWHF speedup approximately 40% when scaling from one two threads 55% increasing three threads. Raspberry Pi 4-based tests IoT have also been conducted, demonstrating function’s effectiveness memory-constrained environments. Statistical demonstrate precision ±0.004, validate hypothesis distribution deviation ±0.05 collision tests, confirming robustness design.

Language: Английский

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Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges

Algorithms, Journal Year: 2025, Volume and Issue: 18(4), P. 209 - 209

Published: April 9, 2025

This paper presents a systematic literature review based on the PRISMA model machine learning-based Distributed Denial of Service (DDoS) attacks in Internet Things (IoT) networks. The primary objective is to compare research trends deployment options, datasets, and learning techniques used domain between 2019 2024. results highlight dominance certain datasets (BoT-IoT TON_IoT) combination with Decision Tree (DT) Random Forest (RF) models, achieving high median accuracy rates (>99%). discusses various that are train evaluate (ML) models for detecting networks how they impact performance. Furthermore, findings suggest due hardware limitations, there preference lightweight ML solutions preprocessed datasets. Current indicate larger or industry-specific will continue gain popularity alongside more complex such as deep learning. emphasizes need robust scalable Software-Defined Networks (SDNs) offering flexibility, edge computing being extensively explored cloud environments, blockchain-integrated emerging promising approach enhancing security.

Language: Английский

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Applications and Emerging Trends of Blockchain Technology in Marketing to Develop Industry 5.0 Businesses: A Comprehensive Survey and Network Analysis

Internet of Things, Journal Year: 2024, Volume and Issue: 28, P. 101401 - 101401

Published: Oct. 11, 2024

Language: Английский

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