Aug 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 1193-1200· 0 citations
TL;DR
Results indicate that combining tiered trust evaluation with machine learning based classification yields a measurably more scalable and resilient security layer for 6G-enabled IoT deployments than existing static or purely cryptographic approaches.
Abstract
The rapid proliferation of Internet of Things (IoT) devices under sixth-generation (6G) networks introduces a highly
dynamic, decentralized environment in which static, perimeter-based security models are no longer adequate. This paper
proposes AZTM-v3 an adaptive Zero Trust framework that couples behavior-driven trust management with a Random Forest
classifier to identify and isolate malicious nodes in real time. The framework is evaluated on an NS-3 simulation of a 150-node 6G
IoT network subjected to Sybil, Denial-of-Service (DoS), spoofing, replay and ON-OFF attacks. Unlike prior trust-management
proposals that report only qualitative or partial outcomes this work quantifies performance across five dimensions i.e detection
accuracy, F1-score, false-positive rate, end-to-end latency and consensus-convergence time and benchmarks AZTM-v3 against
PKI-based, centralized-trust and static-blockchain baselines. AZTM-v3 attains a 98.1% overall detection accuracy with a 1.6%
false-positive rate at 150 nodes and sustains 95.4% accuracy at 200 nodes outperforming the PKI baseline by 12–18 percentage
points across all tested loads. These results indicate that combining tiered trust evaluation with machine learning based
classification yields a measurably more scalable and resilient security layer for 6G-enabled IoT deployments than existing static
or purely cryptographic approaches.
This work demonstrates that a dynamic, reputation-based security layer can provide near-total protection against the modeled threats at negligible performance cost, offering a viable, highly effective solution for securing resource-constrained IoT deployments.
Natraj N. A., A. T, B. M.· Discover Internet of Things· 0 citations
BELS-IoT is proposed, a novel decentralized protection architecture that integrates a cryptocurrency-based blockchain layer with a multi-layer ensemble learning engine that rewards honest behavior and penalizes malicious activities while maintaining privacy through federated learning with blockchain-verified reputation scores.
Anwar Kalghoum, Leila Azouz Saidane· SN Computer Science· 0 citations
This paper proposes a federated intrusion-detection framework for autonomous-vehicle Internet of Things (IoT) networks in future 6G transport systems. Here, IoT refers to the connected sensors, vehicles, roadside units, and control platforms that continuously exchange operational data. The framework, FL-AIID-AV, allows vehicles to train a shared detection model without sending raw traffic or sensor data to a central server. Its secure aggregation mechanism for autonomous vehicle (SecAggAV), combines anomaly screening of model updates, history-based trust weighting, client-side differential privacy, encrypted update transport, and latency-aware round control. In the current implementation, the aggregation server is trusted for decryption, anomaly scoring, and trust computation, while encryption protects updates in transit. This trusted-server assumption is intended as a pragmatic transitional design choice for near-term edge-cloud 6G deployments rather than as the end-state of a fully decentralised 6G architecture; the same trust-weighting logic can later be migrated to server-opaque or multi-coordinator secure aggregation. On the CICIoV2024 benchmark, the method reaches 98.2% accuracy in benign settings and 95.4% under 20% poisoning, with edge inference latency below 50 ms. We additionally evaluate the method on TON-IoT, Edge-IIoTset, and a strict de-duplicated CICIoV2024 split. Under this complementary evaluation track, SecAggAV attains 97.42% accuracy on TON-IoT, 95.84% on Edge-IIoTset, and a macro-F1 score, that is, the unweighted average F1 across classes, of 0.712 on the de-duplicated CICIoV2024 corpus. Overall, the results show that the proposed framework remains robust under heterogeneous data, adversarial updates, and latency constraints while providing better visibility into minority-attack detection.
Q. Mamun, Md. Mujibur Rahman, Mehedi Hasan et al.· Discover Computing· 0 citations
The rapid development of the Internet of Things (IoT) has placed considerable pressure on both security and stability in heterogeneous, resource-constrained networks. In such dynamic environments, trust management is a central issue to determine which service providers can be trusted and to combat malicious activity. Although blockchain-based solutions have offered a means for decentralized, tamper-resistant trust management, most rely on classical cryptographic primitives, which are vulnerable to future quantum computing attacks. This study proposes a Quantum-Resistant Blockchain-Based Trust Management (QR-BCTM) framework in which Post-Quantum Cryptographic mechanisms, Permissioned Blockchain Platform, and Fog-assisted Trust Management architecture are combined and utilized in IoT networks. The framework introduces a quantum-aware trust computation model that combines behavioral trust, indirect recommendations, and a cryptographic assurance score quantifying each participant’s compliance with security requirements. Trust evidence is compressed to reduce blockchain storage and communication overhead, while the hierarchical fog-blockchain architecture offloads computationally intensive operations from resource-constrained IoT devices. The performance of the framework has been simulated in the presence of an adversary, including bad-mouthing, ballot-stuffing, on-off behavior, and identity attacks using a Sybil-type mechanism. Trust accuracy, false trust acceptance, communication overhead, and computation cost were measured, and a sensitivity analysis on the trust-weight parameters was performed. The simulation results suggest that QR-BCTM can enhance the accuracy of trust evaluation, mitigate the impact of malicious nodes, and remain scalable and efficient despite the existing cryptographic overhead. Post-quantum digital signatures and formal security analysis provide protection against quantum-era threats and attacks, while classical threats are mitigated through behavioral trust aggregation and recommendation filtering. In summary, QR-BCTM provides a scalable, simulation-validated and quantum-aware framework for trustworthy IoT network operation, offering practical guidelines for future deployment and prototyping.
M. A. Al-Khasawneh, D. Alsekait, K. Alkayid et al.· Scientific Reports· 0 citations
These findings demonstrate that Edge-ZTA provides an efficient, privacy-preserving, and scalable cybersecurity framework capable of mitigating sophisticated multi-stage cyberattacks while satisfying the stringent performance requirements of next-generation Industrial IoT infrastructures.
Ahmed Ramzi Rashid, Zaydon L. Ali, Ahmed Sedeeq Baker Al-Doori· Al-Noor Journal of Engineeri...· 0 citations
New computing environments such as the cloud, edge, and Internet of Things (IoT) introduce new challenges in trust management, security, and energy efficiency. Centralized trust management systems focus on a single point of failure, making them less transparent and more vulnerable to attack. This paper presents the first trust framework based on blockchain technology to support security and sustainability in distributed computing systems. The methodology presented in this paper combines distributed ledger technology, smart contracts, and a lightweight consensus mechanism to provide a transparent and computationally inexpensive way to trust evaluation. To evaluate trust dynamically at a given node, a multi-factor trust model is used that incorporates trust, security, behavioral factors, and energy efficiency. A distributed system with 100 nodes and 1000 transactions per node was used to evaluate the framework experimentally. Results indicated that the proposed system attained a trust score of 95%, and performed better than centralized (82%) and reputation-based (88%) models. The system also improved energy efficiency by 26.7% relative to baseline systems, indicating that the computing system can be integrated to enhance sustainability. The blockchain framework trust models provided security and energy savings in distributed systems. It also presented robust designs and useful computational environments. Future extensions of this work will focus on Artificial Intelligence (AI) based trust prediction and on determining hybrid blockchains for performance improvements.
Priyanka Singh, Sonam Dubey· 2026 4th International Confe...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
