Blockchain Papers

Mohamed Boudra, Ahmed Bendahmane

No abstract is available for this record.

Yuji Sang, Chenglong Xu, Long Lv, Lijun Liu · 5 authors

Abstract Aiming at the security issues of open channel vulnerability, limited node resources, and single-point failure caused by centralized authentication in unmanned aerial vehicle (UAV) swarm networks, this paper proposes an authentication and key agreement scheme integrating Physical Unclonable Function (PUF), Chebyshev chaotic map and blockchain. The scheme constructs an integrated architecture of physical security, lightweight encryption and distributed trust, which supports mutual authentication in dual scenarios of UAV-Ground Control Station (GCS) and UAV-UAV. Decentralized trusted authentication is realized via blockchain and smart contracts, ensuring that authentication information is tamper-proof and traceable. Formal security verification based on the ROR model and informal analysis demonstrate that the proposed scheme satisfies multiple security requirements including anonymity and forward secrecy, and can resist common attacks such as replay attack, man-in-the-middle attack and physical capture attack. Performance evaluation results indicate that the scheme completes authentication with only two rounds of interaction. Its computational and communication overheads are significantly lower than those of existing schemes, making it suitable for resource-constrained UAV swarms.

Mohamed Boudra, Ahmed Bendahmane

No abstract is available for this record.

Wisam Makki Alwash, Weam Husham Aljabbari, Muhammed Ali Aydın, Hasan H. Balık

Internet of Vehicles (IoV) intrusion detection systems (IDSs) require collaborative learning that preserves raw-data locality while producing independently checkable post-run evidence. This paper presents FL-BC-IDS, an evidence-native, privacy-aware hierarchical federated IDS in which vehicles train Differentially Private XGBoost models, roadside units perform deterministic admission and tree-bagging aggregation, and the GLOBAL stage forms an equal-weight ensemble over validated RSU models. Signed reports, privacy records, SHA-256/Poseidon commitments, scoped Groth16 proofs, reconstructable public inputs, and digest-pinned blockchain receipts provide a unified verification path. Across 10 seed-controlled runs, the mean ± SD accuracy/F1 values were 0.998021±0.000246/0.983597±0.002053 on CSE-CIC-IDS2018 and 0.999867±0.000152/0.999495±0.000579 on CICIoV2024. With thresholds fixed exclusively from development data, the strict held-out-attack macro recall was 0.8031 and 0.9090 on CSE-CIC-IDS2018 and CICIoV2024, respectively, indicating residual attack-specific generalization limitations; supervised rolling-origin temporal refresh on CSE-CIC-IDS2018 achieved 0.984788 pooled seen-attack recall at a 0.005700 test FPR. A controlled 20-vehicle, eight-round heterogeneity and participation stress test retained 0.998151 accuracy and 0.984782 F1-score. Verification rejected invalid or context-mismatched artifacts and independently checked model–anchor consistency, RSU aggregation replay, commitments, and public inputs. The reported DP budgets are conditional learner-stage bounds for learner-input record instances, not end-to-end guarantees for original pre-preprocessing records.

Md Shahanur Islam Shagor

No abstract is available for this record.

Authors unavailable

In the age of fast industrial digitalization, securing the heterogeneous and high-volume data produced by the Industrial IoT systems is a basic need. The chapter is dedicated to the application of machine learning and deep learning methods in the process of securing multimodal data within the context of Industrial Internet of Things (IIoT). It includes a detailed discussion of multimodal sources of data and the corresponding cyber threat environment, and then it introduces the machine learning (ML)-based and deep learning (DL)-based anomaly detection and intrusion prevention techniques. The chapter reviews the secure architectural designs, which combine edge, fog, and cloud intelligence and privacy-sensitive and trust management schemes like federated learning and blockchain. The practical applicability of such approaches is pointed out by the real-life industrial applications and case studies. The main implementation issues and the performance evaluation metrics are examined to ensure a successful implementation. The chapter ends by highlighting the future directions and new trends, focusing on adaptive, explainable, and resilient intelligent security solutions in next-generation IoT systems of the industrial world.

Syed Ahmed Nasir Abdi

No abstract is available for this record.

Sameen Fatima, Muhammad Junaid Arshad

The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain.

John Alexander Taborda, Cesar Enrique Polo Castro, Alexander Armando Bustamante, Holman Dario Bustos

The transition toward decentralized renewable energy systems creates monitoring problems that current digital infrastructures do not solve: sustainability claims are produced by the same actors they evaluate, environmental evidence is reported periodically rather than observed continuously, and the communities most affected by deployment cannot inspect the data used to represent their territories. Existing integrated platforms combine subsets of the blockchain, Internet of Things (IoT) sensing and life cycle assessment (LCA) at the data layer, but they do not organize that integration through an explicit governance structure. This paper contributes a cybernetic governance framework in which the Viable System Model (VSM) supplies the organizing structure of a blockchain–IoT–LCA monitoring architecture, so that sensing, distributed trust, strategic intelligence and participatory governance are recursively coupled rather than sequentially chained. The framework was developed and evaluated under the Design Science Research paradigm, and instantiated in the IMPACT Energy.CO platform across two technology routes, wind and solar, in La Guajira, Cesar, Atlántico and Magdalena, Colombia. Evaluation against six pre-declared criteria reports 45 executed test cases with a 100% pass rate, 90% unit and 87% integration code coverage, load tests up to 5000 concurrent users with zero errors and sub-second mean response, an operating hash-chained provenance layer issuing verifiable LCA certificates, 14 participatory validation workshops, 199 users trained and 166 technicians certified. We use traceability in a deliberately narrow sense throughout: the property whereby a committed record can be linked to the ingested data series, model version and computation that produced it, and its integrity and ordering checked by a party that does not trust the producer. It is provenance and integrity traceability from the point of ingestion onward, and it is not metrological traceability: the architecture cannot verify that an original sensor measurement corresponds to the physical quantity it purports to represent. We accordingly make explicit what the architecture does not guarantee: a ledger protects records after commitment but cannot certify measurement at the point of capture, and we present a threat model, a set of implemented controls and the residual risk that remains. This study contributes an architecture, a reproducible development and evaluation method, and a calibrated account of what verifiable environmental monitoring can and cannot deliver in contested Global-South territories.

Haitao Zhang, Jingtao Chen, Gaoyu Liu, Fanyu Yang · 5 authors

Digital-twin-enabled human–machine collaboration (HMC) has increasingly been proposed as a system-level approach for connecting human operators, robots, sensors, artificial intelligence modules, and manufacturing resources. However, the literature varies substantially in what is called a digital twin, how physical and virtual models are coupled, whether models are updated from physical data, and how far systems have progressed beyond simulation or controlled laboratory demonstrations. This structured integrative review examines the conditions under which a digital twin can function as an integration layer for HMC in sustainable smart manufacturing, rather than assuming that such integration is already established industrial practice. The literature corpus was assembled through searches of the Web of Science Core Collection, Scopus, and IEEE Xplore, complemented by Google Scholar-based citation tracking and backward and forward citation tracing. The core search focused on studies published from 1 January 2020 to 5 August 2026, while earlier seminal studies were retained to support definitions and historical context. Studies were screened using explicit criteria for manufacturing relevance, physical–virtual coupling, state synchronization or model updating, feedback capability, and validation setting, and were critically coded by model type, integration mechanism, deployment maturity, and sustainability evidence. The review compares multimodal perception and human-state modeling, intention understanding and augmented interaction, task allocation and shared planning, digital-twin architectures, adaptive control and safety verification, and human–AI decision-making. The evidence indicates that digital twins are promising as coordination and verification layers, but many reported systems remain conceptual, simulation-based, or limited to controlled physical prototypes. Key barriers include model fidelity, online model updating, real-time synchronization, cross-platform interoperability, safety assurance, human-data governance, and the limited availability of directly measured sustainability outcomes. Future work should prioritize validated hybrid models, traceable model-update mechanisms, staged virtual-to-physical deployment, interoperable data contracts, and longitudinal evaluation of technical, human, economic, and environmental performance.

ZHU ZHAORUI

The integration of real-time capacity optimization suites within legacy civil aviation computing ecosystems is highly bottlenecked by the severe structural heterogeneity of distribution data infrastructures. Telemetry and transactional feeds remain highly siloed across disparate legacy Global Distribution System (GDS) alphabetic fields, Low-Cost Carrier (LCC) direct APIs, and non-public multi-alliance loyalty program ledger inventories. This paper presents a sovereign computational architecture engineered to achieve distributed eventual consistency across these fragmented environments. The system introduces an automated Heterogeneous Data Fusion (H-Pipeline) layer that aggregates high-frequency multi-source distribution data streams into a unified, encrypted semantic vector space through specialized vector dimension coalescing protocols operating under strict TLS 1.3 mutual authentication frameworks. To intelligently parse and navigate these multi-source streams, the architecture deploys an asynchronous, cloud-native Large Language Model (LLM) orchestration middleware running entirely within serverless stateless edge containers (AWS Wavelength/Cloudflare Workers meshes). The cloud-native LLM layer is established as an asynchronous predictive semantic router, dynamically identifying macroeconomic anomalies, unexpected capacity imbalances, and transient route volatility without introducing synchronized write-back overhead or data persistence bottlenecks to critical On-Line Transaction Processing (OLTP) reservation threads. Simulation-based performance evaluation utilizing industry-standard benchmark datasets confirms single-digit millisecond failover recovery bounds, a strict 12 ms cross-border fiber pathway propagation convergence limit, and total mitigation of cross-region distributed semantic drift, establishing a robust computational foundation for next-generation asynchronous AI airline operations.

Jasem M. Alostad, R. Gopi, A. Ananthi, B. Sathiya

No abstract is available for this record.

VA Sharma

The paper provides the Abelian and Tauberian theorems for the generalized Mellin-Whittaker transform. The asymptotic results obtained here are also relevant to emerging interdisciplinary applications that rely on transform methods for the analysis of complex dynamical systems, including smart logistics ecosystems that converge the Internet of Things, artificial intelligence and quantum computing, human-centric automation under the Industry 5.0 paradigm, resilient and adaptive global supply chains enabled by digital-twin technology, and quantum-assisted optimization frameworks for transportation and logistics. Further connections are drawn to battery-management algorithms for electric and hybrid vehicles, blockchain-secured supply-chain transparency, and systematic reviews of supply-chain resilience in the era of digital transformation. In the quantum-computing literature the short-time embedding of continuous dynamics into discrete Ising or QUBO Hamiltonians is a recognized bottleneck. The initial-value theorem guarantees that the leading order asymptotic of the physical signal is correctly represented by the lowest-order terms of the quantum Hamiltonian, thereby improving the quality of the solutions returned by quantum annealers and variational quantum algorithms alike. Key-words: edge computing, IoT-enabled logistics, multi-agent logistics, multi-commodity flows, edge computing, IoT-enabled networks, real-time decision support, digital-twin consistency, supply-chain resilience, Industry 5.0 automation, blockchain audit trails, smart mobility, battery residual capacity, green logistics.

Shritik Raj, M. Madiajagan

Recent intrusion detection systems (IDS) increasingly rely on machine learning (ML) and deep learning techniques to detect sophisticated cyberattacks. However, many existing frameworks still suffer from limited explainability, black-box decision-making, and the absence of secure trust verification mechanisms for intrusion records. To address these challenges, this paper proposes TAE-IDS, a Trust-Aware Explainable Intrusion Detection Framework that integrates attention-based meta-ensemble learning, SHapley Additive exPlanations (SHAP)-driven explainability, and blockchain-inspired tamper-evident validation within a unified cybersecurity architecture. The proposed framework employs heterogeneous base classifiers, namely Logistic Regression (LR), Extra Trees (ET), and XGBoost (XGB), to capture diverse network traffic characteristics. Uncertainty-aware meta-features, including logits, confidence scores, and entropy representations, are extracted from the base learners and processed by an adaptive Bidirectional Long Short-Term Memory (BiLSTM) attention-based meta-classifier for contextual intrusion reasoning and adaptive ensemble aggregation. To enhance transparency and analyst trust, SHAP-based explainability is incorporated to provide both global and local interpretations of intrusion predictions. Furthermore, a blockchain-inspired tamper-evident validation mechanism based on SHA-256 cryptographic hashing is integrated to enable tamper-proof intrusion logging, immutable auditing, and secure forensic verification of IDS outputs. The proposed framework was evaluated on the UNSW-NB15 and CICIDS2017 benchmark datasets under both binary and multiclass intrusion detection settings. Experimental results demonstrate that TAE-IDS achieves strong intrusion detection performance, interpretable intrusion reasoning, and effective blockchain-assisted tamper-evident validation on the evaluated benchmark datasets. The integration of explainable artificial intelligence (XAI) and blockchain-assisted validation enhances transparency, forensic traceability, and the integrity of intrusion records while providing a foundation for future validation in operational network environments.

T G Keshavamurthy, S. Guruprasad, K. N. Hareesh, M. V. Chidananda Murthy · 6 authors

In recent years, smart IoT systems pose significant challenges regarding security, scalability, and intelligent decision-making for IoT data, particularly amid advances in quantum computing attacks. Traditional cryptographic and machine learning techniques seem insufficient for real-time IoT systems where data is dynamic and large-scale and security requirements are strict. In this paper, a novel Post-Quantum Probabilistic Hidden-State Deep Learning (PQP_HS_DL) framework is presented that comprises of effective probabilistic hidden state modelling, lattice-based post-quantum cryptography and blockchain technology to process IoT data securely and efficiently. In the proposed PQP_HS_DL framework, a probabilistic hidden state model is applied to learn temporal dynamics and uncertainty in IoT data streams to provide enhanced prediction and reliable anomaly detection capabilities. A lattice-based cryptographic scheme ensures quantum-resistant security, and blockchain provides data integrity, transparency, and decentralized trusted authority management. The system is further enhanced by edge computing to alleviate latency and realize real-time processing performance. The experimental evaluation of the proposed framework is carried out under 100 IoT nodes to evaluate its performance. The results of the PQP_HS_DL provide a high classification accuracy (97.6%), and higher precision, recall, and F1-score compared to the existing techniques. The latency (72 ms) is lower, the throughput (285 transactions per second) is higher, and energy consumption (0.91) is also effective in the PQP_HS_DL framework for real-time applications of IoT. Security analysis shows that the entropy (0.98) is very high, and the attack probability (0.01) is very low. The framework uses lattice-based post-quantum cryptographic mechanisms, which are effective against any classical attacker as well as against existing quantum cryptanalytic methods, based on standard computational assumptions. Scalability analysis demonstrates that the proposed framework, evaluated with run on IoT networks with over 1000 nodes, exhibits significant performance.

W. C. Yang, J. F. Qiao, J. F. Hu, Jie Wang · 5 authors

This study presents a multi-level verification system for secure communication protocols in energy billing infrastructures. The proposed framework integrates device attestation, network integrity verification, privacy-preserving aggregation, billing validation, and immutable auditing to address security vulnerabilities across Advanced Metering Infrastructure (AMI) communication chains. A Hybrid Secure-Efficient Protocol (HSEP) combining elliptic curve cryptography, homomorphic encryption, and zero-knowledge proofs is developed to provide secure authentication, privacy protection, and verifiable data integrity while maintaining low computational overhead. Experimental evaluation using a large-scale AMI testbed demonstrates that the proposed system significantly improves tampering detection capability, achieving an intrusion detection AUC of 0.94 while maintaining an average energy consumption of 1.55 J per transaction and acceptable communication latency for large-scale deployment. The architecture exhibits strong scalability, robustness, and rapid dispute-resolution performance under multiple attack scenarios. The proposed framework is particularly applicable to wireless smart metering networks and antenna-enabled AMI communication infrastructures, where reliable data transmission, secure protocol verification, and resilience against communication-layer attacks are essential for trustworthy energy billing and grid operation. This work provides an effective engineering solution for secure, privacy-preserving, and verifiable communication in modern intelligent energy systems.

Uzair Aslam Bhatti, Gafur Namazov, Sabirov Sardor, Momogul Ismailova · 7 authors

Decentalized energy systems are a radical departure from the way we have traditionally produced, distributed and consumed electricity – relying upon centralized grids that depend on fossil fuels towards more sustainable, resilient and community-based models. These systems improve grid flexibility, save transmission costs and are fit for renewable power input like solar or wind, since prosumers can generate and distribute energy within the local area. Blockchain technology is going to be a key enabler of this transition, as it offers a reliable, transparent and decentralized platform for energy sharing and grid management. The blockchain technology utilizing the smart contracts and mutual system can provide reliable peer-to-peer power trading, accurate settlement, and less necessity of centralized agency. Its distributed ledger makes the equipment trustable for all participants, and meanwhile it realizes real-time transaction data sharing to optimize grid management like demand response of electricity and certificates tracing of green power production.

Ibrahim Aqeel

No abstract is available for this record.

Sangeetha Nagamani, Annamalai Selvarajan, Raghini Mohan, John Peter Vincent Paul · 6 authors

Secure and transparent water resource management is made possible by blockchain technology and its characteristics such as decentralisation, immutable records, and smart contracts. Tracking water consumption in real time is made possible by collaborating blockchain technology with Internet of Things–based sensors, authenticating data integrity and transparency in fetching records. Blockchain ensures the tamper-proof sustainability of water quality data for pollution prevention. It helps to secure real-time monitoring data permanently, including pH, turbidity, and pollutant levels, which enables regulators to address quality issues. The automation of water rights and allocation transactions in the water trade sector can be processed by blockchain-based smart contracts, which reduce transaction costs and administrative burdens. Water billing, permit issuing, and right transfers, which are part of water trading practices, are guaranteed by digital agreements. As a result, a peer-to-peer water auction can operate with reduced latency and robust fraud prevention. Hence, blockchain applications in water resource management remarkably enhance security, transparency, and efficiency for tracking, controlling pollution, and providing fair water trading through smart contracts.

Mahdiyeh Velaei, Mehdi Aminian, Seyyed Amir Asghari, Mohammad Faraji‐Mehmandar

No abstract is available for this record.

Layla Araiinejad, Vineet Jagadeesan Nair

This perspective examines whether nuclear fusion can provide a scalable, low-carbon power source for rapidly growing AI-driven data center demand. As large language models, cloud computing, and cryptocurrency mining accelerate electricity consumption growth, data centers are projected to account for a substantially larger share of U.S. and global electricity use in the coming decades, creating significant pressure on grid reliability and decarbonization goals. We evaluate the technical and economic alignment between data center load profiles and nuclear power, particularly fusion, through a comparative analysis of capacity factors, levelized cost of electricity, grid interconnection constraints, and deployment pathways. Unlike intermittent renewables, nuclear fission and fusion offer high-capacity-factor, firm baseload generation suited to AI training and inference workloads that require continuous, reliable power. Preliminary techno-economic analysis suggests that several Nth-of-a-kind fusion concepts, particularly magnetic confinement systems, may become cost-competitive with firmed renewable systems and advanced fission for hyperscale data center applications. Co-location of fusion plants with data centers further reduces transmission bottlenecks, improves resilience, and aligns with emerging hyperscaler procurement strategies. We also assess recent regulatory developments and argue that fusion's favorable safety profile and reduced waste burden improve its long-term social and political viability relative to fission. We conclude that fusion represents a strategically important pathway for sustainably powering next-generation computing infrastructure and should be prioritized in both policy and industrial deployment planning.

Binyuan Yan, Zeyuan Zhou, Yun Fu, Yang Su

Multi-heterogeneous power data in smart grid refers to power data that includes multiple types, modalities, and sampling frequencies, such as user electricity consumption, equipment operation, and grid scheduling. The diverse and heterogeneous power data in the smart grid is related to the stable operation of the grid and user privacy. Without effective protection, it is easy to cause risks such as information leakage and scheduling failure. Therefore, targeted security protection solutions need to be constructed. However, there are problems with the loss of information granularity, high risk of privacy leakage, and limited data analysis in the current smart grid power data aggregation and sharing. To enhance the security protection effect of power data, a multi-dimensional data security protection scheme based on data aggregation and Paillier homomorphic encryption is proposed. Firstly, a three-tier system model for multivariate heterogeneous power data in smart grids is constructed (smart meters, data collection stations, and blockchain nodes). Subsequently, the Paillier homomorphic encryption algorithm is integrated to encrypt and aggregate users’ multi-dimensional electricity consumption data. At the same time, data aggregation and consortium chain technology have been introduced. Experimental results demonstrate that this scheme offers significant advantages over traditional Rivest-Shamir-Adleman (RSA) encryption schemes, traditional Advanced Encryption Standard (AES) encryption schemes, Elgamel encryption schemes, and traditional Transmission Control Protocol/Message Queuing Telemetry Transmission Protocol transmission schemes in terms of computational and communication overhead. When the number of users reaches 5000, the computational overhead at data collection stations is only 35.6% of that in traditional RSA methods, and the communication overhead is merely 28.7% of traditional transmission control protocol methods. Additionally, when transmitting power data from 5000 users simultaneously, the information accuracy rate exceeds 92%, and the packet loss rate remains below 0.5%. In conclusion, the proposed scheme provides an efficient and reliable technical pathway for the secure transmission of multivariate heterogeneous power data in smart grids.

MARBIYAT TAHIR GIDADO, BASHIRU ABDULGANIYU, MOHAMMED NASIR MUSA, Umaru Umaru

The increasing digitalization of smart grids has significantly improved the efficiency, reliability, and sustainability of modern power systems. However, the integration of advanced technologies, such as artificial intelligence, the Internet of Things, and cloud computing, has introduced new cybersecurity vulnerabilities that threaten critical energy infrastructure. This study presents a blockchain-enabled privacy-preserving Artificial intelligence framework designed to enhance cybersecurity in smart grid environments, with a particular focus on Northeast Nigeria as a case study. The framework integrates blockchain technology, federated learning, differential privacy, edge computing, and artificial intelligence (AI)-driven intrusion detection into a unified architecture to provide secure, intelligent, and privacy-aware protection for smart grid systems. The proposed framework was developed using the design science research methodology and evaluated through simulation and comparative performance analysis. The framework achieved excellent detection performance with an accuracy of 96.8%, precision of 95.9%, recall of 96.4%, and F1-score of 96.1%, significantly outperforming conventional centralized AI and blockchain-only approaches. The integration of federated learning and differential privacy effectively protected consumer information with a privacy leakage rate of only 2.7% while maintaining high model utility of 94.8%. The blockchain performance evaluation showed a transaction latency of 184.6 Ms, a throughput of 421.3 transactions per second, and efficient smart contract execution. The suitability of the framework for practical deployment with moderate resource requirements by computational assessment. The findings demonstrate that combining blockchain, privacy-preserving learning, and AI provides a comprehensive, scalable, and resilient cybersecurity solution for SGIs. This study contributes to the growing body of knowledge on smart grid cybersecurity and offers practical insights for utility providers, researchers, and policymakers seeking to strengthen the security and resilience of emerging smart grid systems, particularly in developing regions with infrastructural challenges.

Shilpa Bhatia, Ramesh Chandra Sahoo, Arvind Kumar

Cloud computing infrastructures are facing serious risks from Distributed-Denial-of Service attacks, which include historically high attack volumes and ineffectiveness of conventional defensive strategies. The usefulness of blockchain based security measures in detecting and preventing DDoS attacks on cloud computing infrastructure is covered in this paper. Analyzed a hybrid approach that integrated distributed ledger technology with smart contracts for the identification of attack patterns across 50 enterprise cloud environments over a period of 18 months. Our results show a reduction of false positives by as much as 87% for blockchain-based validation compared to the conventional approach and a 94% success rate in the detection of advanced DDoS variants. The response time remained, on average, around 2.3 seconds during the high-volume attack in comparison to traditional centralized solutions. The results in the paper gives an idea that an immutable and distributed consensus characteristic based on blockchain provides robust defenses against modern DDoS threats. This work represents a growing body of evidence supporting the validity of incorporating blockchain into the architecture of the next generation of cloud-based security.