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.
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.
Abstract Vehicle-to-everything (V2X) systems combine safety-relevant telemetry, wireless communication, distributed identity, collaborative learning, and long-lived cryptographic trust, creating security dependencies that are difficult to evaluate when each mechanism is studied in isolation. This paper presents OmniGuard V2X, a six-layer research prototype that integrates post-quantum key establishment and signatures, privacy commitments, decentralized identity, a permissioned tamper-evident ledger, federated-learning experimentation, anomaly hooks, and authenticated runtime services in a unified C++/Go/Python stack. The contribution is an assumption-aware validation model rather than a new cryptographic primitive. The supported native path uses real ML-KEM-512 and ML-DSA-44 operations, while classical Pedersen and Schnorr-style assumptions are explicitly excluded from end-to-end post-quantum claims. The prototype further implements encrypted persistent PQC identity material, stable key identifiers, guarded rotation, signed key-transition evidence, bounded historical signature verification, fail-closed provider selection, replay-aware local control, and authenticated rollback-state checks. A retained warm-start pipeline measurement is 5.34 ms in one prototype environment and is reported only as a descriptive local result. Existing anomaly and federated-learning experiments are classified as sanity checks because they do not yet support statistically defensible robustness metrics. The study shows how machine-readable security boundaries and evidence-aware claim control can improve the reproducibility and interpretability of multi-mechanism V2X security prototypes.
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.