A novel deep learning-based intrusion detection system for IoT DDoS security

Internet of Things, Год журнала: 2024, Номер 28, С. 101336 - 101336

Опубликована: Авг. 29, 2024

Язык: Английский

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CO-STOP: A Robust P4-Powered Adaptive Framework for Comprehensive Detection and Mitigation of Coordinated and Multi-Faceted Attacks in SD-IoT Networks

Computers & Security, Год журнала: 2025, Номер unknown, С. 104349 - 104349

Опубликована: Янв. 1, 2025

Язык: Английский

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Blockchain-inspired distributed security framework for Internet of Things

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 24, 2025

The rapid proliferation of mobile IoT devices with inadequate security measures has elevated to a critical concern. Researchers have proposed various systems for vulnerability detection based on conventional frameworks. However, these approaches often face challenges such as high computational costs, limited storage capacity, and slow response times. To ensure robust protection against cyberattacks, modern solutions must continuously monitor analyze historical data across the entire network. This paper introduces distributed framework networks, leveraging software-defined networking (SDN), blockchain, edge computing efficiently detect mitigate IoT-based attacks. In framework, SDN facilitates network-wide monitoring analysis, enabling effective attack detection. Blockchain technology ensures decentralized tamper-resistant identification, addressing potential vulnerabilities. Meanwhile, paradigm enables real-time at network edge, ensuring timely alerts. An experimental evaluation demonstrates its superiority over traditional in terms accuracy (98.7%), false positive rate (1.2%) time (101.1 ms), highlighting effectiveness securing networks.

Язык: Английский

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

Algorithms, Год журнала: 2025, Номер 18(4), С. 209 - 209

Опубликована: Апрель 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.

Язык: Английский

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“LBTMA: An Integrated P4-Enabled Framework for Optimized Traffic Management in SD-IoT Networks”

Internet of Things, Год журнала: 2024, Номер 28, С. 101432 - 101432

Опубликована: Ноя. 13, 2024

Язык: Английский

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