Cybersecurity Solutions for Industrial Internet of Things–Edge Computing Integration: Challenges, Threats, and Future Directions

Sensors, Journal Year: 2025, Volume and Issue: 25(1), P. 213 - 213

Published: Jan. 2, 2025

This paper provides the complete details of current challenges and solutions in cybersecurity cyber-physical systems (CPS) within context IIoT its integration with edge computing (IIoT–edge computing). We systematically collected analyzed relevant literature from past five years, applying a rigorous methodology to identify key sources. Our study highlights prevalent layer attacks, common intrusion methods, critical threats facing IIoT–edge environments. Additionally, we examine various types cyberattacks targeting CPS, outlining their significant impact on industrial operations. A detailed taxonomy primary security mechanisms for CPS is developed, followed by comparative analysis our approach against existing research. The findings underscore widespread vulnerabilities across architecture, particularly relation DoS, ransomware, malware, MITM attacks. review emphasizes advanced technologies, including machine learning (ML), federated (FL), blockchain, blockchain–ML, deep (DL), encryption, cryptography, IT/OT convergence, digital twins, as essential enhancing real-time data protection computing. Finally, outlines potential future research directions aimed at advancing this rapidly evolving domain.

Language: Английский

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

Internet of Things, Journal Year: 2024, Volume and Issue: 28, P. 101336 - 101336

Published: Aug. 29, 2024

Language: Английский

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Securing Industry 5.0: An explainable deep learning model for intrusion detection in cyber-physical systems

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110161 - 110161

Published: Feb. 21, 2025

Language: Английский

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FP-growth-based signature extraction and unknown variants of DoS/DDoS attack detection on real-time data stream

Journal of Information Security and Applications, Journal Year: 2025, Volume and Issue: 89, P. 103996 - 103996

Published: Feb. 7, 2025

Language: Английский

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SPARK and SAD: Leading-Edge Deep Learning Frameworks for Robust and Effective Intrusion Detection in SCADA Systems

International Journal of Critical Infrastructure Protection, Journal Year: 2025, Volume and Issue: unknown, P. 100759 - 100759

Published: March 1, 2025

Language: Английский

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Multi-attention DeepCRNN: an efficient and explainable intrusion detection framework for Internet of Medical Things environments

Knowledge and Information Systems, Journal Year: 2025, Volume and Issue: unknown

Published: April 5, 2025

Language: Английский

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MULTI-BLOCK: A novel ML-based intrusion detection framework for SDN-enabled IoT networks using new pyramidal structure

Internet of Things, Journal Year: 2024, Volume and Issue: 26, P. 101231 - 101231

Published: May 19, 2024

Language: Английский

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Deep learning-empowered intrusion detection framework for the Internet of Medical Things environment

Knowledge and Information Systems, Journal Year: 2024, Volume and Issue: 66(10), P. 6001 - 6050

Published: June 10, 2024

Language: Английский

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DDoS Attacks Detection based on Machine Learning Algorithms in IoT Environments

INTELIGENCIA ARTIFICIAL, Journal Year: 2024, Volume and Issue: 27(74), P. 152 - 165

Published: July 11, 2024

In today’s digital era, most electrical gadgets have become smart, and the great majority of them can connect to internet. The Internet Things (IoT) refers a network comprised interconnected items. Cloud-based IoT infrastructures are vulnerable Distributed Denial Service (DDoS) attacks. Despite fact that these devices may be accessed from anywhere, they assault compromise. DDoS attacks pose significant threat security operational integrity. in which infected botnets networks hit victim’s PC several systems across internet, is one popular. this paper, three prominent datasets: UNSW-NB 15, UNSW-2018 Botnet recent Edge IIoT using an Anomaly-based Intrusion Detection system(AIDS) detect mitigate AIDS employ machine learning methods Deep Learning (DL) for attack mitigation. suggested work employed different types (DL): Random Forest (RF), Support Vector Machine (SVM), Logistic Regression, Multi-layer perceptron (MLP), deep Artificial Neural Network (ANN), Long Term Short Memory (LSTM) identify Both contrasted by database stores trained signatures. As results, RF shows promising performance with 100% accuracy minimum false positive on testing both datasets 15 Botnet. addition, results realistic dataset show good 98.79% LSTM 99.36% time compared other multi-class detection.

Language: 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, Journal Year: 2025, Volume and Issue: unknown, P. 104349 - 104349

Published: Jan. 1, 2025

Language: Английский

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