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

Sensors, Год журнала: 2025, Номер 25(1), С. 213 - 213

Опубликована: Янв. 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.

Язык: Английский

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Secure IoT sensor networks through advanced anomaly detection with Kolmogorov–Arnold Networks (KANs)

Microsystem Technologies, Год журнала: 2025, Номер unknown

Опубликована: Янв. 21, 2025

Язык: Английский

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A Literature Review on Security in the Internet of Things: Identifying and Analysing Critical Categories

Computers, Год журнала: 2025, Номер 14(2), С. 61 - 61

Опубликована: Фев. 11, 2025

With the proliferation of IoT-based applications, security requirements are becoming increasingly stringent. Given diversity such systems, selecting most appropriate solutions and technologies to address challenges is a complex activity. This paper provides an exhaustive evaluation existing related IoT domain, analysing studies published between 2021 2025. review explores evolving landscape security, identifying key focus areas, challenges, proposed as presented in recent research. Through this analysis, categorizes efforts into six main areas: emerging (35.2% studies), securing identity management (19.3%), attack detection (17.9%), data protection (8.3%), communication networking (13.8%), risk (5.5%). These percentages highlight research community’s indicate areas requiring further investigation. From leveraging machine learning blockchain for anomaly real-time threat response optimising lightweight algorithms resource-limited devices, researchers propose innovative adaptive threats. The underscores integration advanced enhance system while also highlighting ongoing challenges. concludes with synthesis threats each identified category, along their solutions, aiming support decision-making during design approach applications guide future toward comprehensive efficient frameworks.

Язык: Английский

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Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 2, 2024

Язык: Английский

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Optimizing cryptographic protocols against side channel attacks using WGAN-GP and genetic algorithms

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

Опубликована: Янв. 16, 2025

This research introduces a novel hybrid cryptographic framework that combines traditional protocols with advanced methodologies, specifically Wasserstein Generative Adversarial Networks Gradient Penalty (WGAN-GP) and Genetic Algorithms (GA). We evaluated several protocols, including AES-ECB, AES-GCM, ChaCha20, RSA, ECC, against critical metrics such as security level, efficiency, side-channel resistance, cryptanalysis resistance. Our findings demonstrate this integrated approach significantly enhances both efficiency across all protocols. Notably, the AES-GCM algorithm exhibited superior performance, achieving minimal computation time robust study underscores potential of leveraging machine learning evolutionary algorithms to advance protocol laying foundation for future advancements in cybersecurity.

Язык: Английский

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A Review of the Authentication Techniques for Internet of Things Devices in Smart Cities: Opportunities, Challenges, and Future Directions

Sensors, Год журнала: 2025, Номер 25(6), С. 1649 - 1649

Опубликована: Март 7, 2025

Smart cities have witnessed a transformation in urban living through the Internet of Things (IoT), which has improved connectedness, efficiency, and sustainability. However, adoption IoT devices presents significant security vulnerabilities, particularly authentication. The specific limitations contexts, such as constrained computational resources, are frequently not adequately addressed by traditional authentication techniques. existing methods used for smart critically examined this review study. We evaluate advantages disadvantages each mechanism, emphasizing real-world applicability. Additionally, we examine cutting-edge developments that offer scalability, blockchain technology, biometric authentication, machine learning-based solutions. This study aims to identify gaps propose future research directions develop robust frameworks protect user privacy data integrity.

Язык: Английский

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Cybersecurity Threat Modeling for IoT-Integrated Smart Solar Energy Systems: Strengthening Resilience for Global Energy Sustainability

Sustainability, Год журнала: 2025, Номер 17(6), С. 2386 - 2386

Опубликована: Март 9, 2025

The integration of Internet Things (IoT) technologies into solar energy systems has transformed them smart systems, enabling advanced real-time monitoring, control, and optimization. However, this connectivity also expands the attack surface, exposing critical components to cybersecurity threats that could compromise system reliability long-term sustainability. This study presents a comprehensive threat modeling analysis for IoT-based using STRIDE model systematically identify, categorize, assess potential security risks. These risks, if unmitigated, disrupt operations hinder large-scale adoption energy. methodology begins with use case outlining architecture key components, including sensors, PV modules, IoT nodes, gateways, cloud infrastructure, remote-access interfaces. A Data Flow Diagram (DFD) was developed visualize data flow identify trust boundaries. applied classify threats, such as spoofing, tampering, repudiation, information disclosure, denial service, elevation privilege across their interactions. DREAD risk assessment then used prioritize based on Damage Potential, Reproducibility, Exploitability, Affected Users, Disability. results indicate most fall high-risk category, scores ranging from 2.6 2.8, emphasizing need targeted mitigation. proposes recommendations address identified enhance resilience IoT-enabled systems. By securing these infrastructures, research supports transition sustainable by ensuring integrity protection against cyber threats. combined provides robust framework identifying, categorizing, prioritizing effective resource allocation measures. findings offer insights safeguarding renewable evolving contributing global sustainability goals in an increasingly interconnected world.

Язык: Английский

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Maximizing healthcare security outcomes through AI/ML multi-label classification approach on IoHT devices

Health and Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 8, 2025

Язык: Английский

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Empirical evaluation of ensemble learning and hybrid CNN-LSTM for IoT threat detection on heterogeneous datasets

The Journal of Supercomputing, Год журнала: 2025, Номер 81(6)

Опубликована: Апрель 23, 2025

Язык: Английский

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A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study

Applied Sciences, Год журнала: 2024, Номер 14(24), С. 11545 - 11545

Опубликована: Дек. 11, 2024

The growth of the Internet Things (IoT) and its integration with Industry 4.0 5.0 are generating new security challenges. One key elements IoT systems is effective anomaly detection, which identifies abnormal behavior in devices or entire systems. This paper presents a comprehensive overview existing methods for detection networks using machine learning (ML). A detailed analysis various ML algorithms, both supervised (e.g., Random Forest, Gradient Boosting, SVM) unsupervised Isolation Autoencoder), was conducted. results tests conducted on popular datasets (IoT-23 CICIoT-2023) were collected analyzed detail. performance selected algorithms evaluated commonly used metrics (Accuracy, Precision, Recall, F1-score). experimental showed that Forest Autoencoder highly detecting anomalies. article highlights importance appropriate data preprocessing to improve accuracy. Furthermore, limitations centralized approach context distributed discussed. also potential directions future research field IoT.

Язык: Английский

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