Search Results (537)

IoT-connected healthcare sensor networks require authenticated and privacy-preserving data exchange among wearable sensors, mobile medical terminals, edge gateways, cloud servers, and medical institutions. Existing authentication schemes for healthcare IoT often bind signatures directly to user identities, exposing sensitive personal or institutional information and imposing heavy verification costs on resource-constrained sensing devices. To address this problem, we propose a fine-grained and flexible dual authentication scheme for healthcare sensor networks. In the proposed scheme, health data and diagnoses are signed with a fresh signing key and a fine-grained access control policy each time, so that the signer identity remains hidden while authorized entities can still modify permitted parts of signed data. No entity other than an authorized entity can trace a malicious signer or modify signed data without changing the data source. To support lightweight verification in sensor-edge-cloud deployments, we further present a verifiable outsourced authentication scheme that outsources time-consuming pairing operations to cloud servers; the online verification process then requires only six multiplication operations. As a fundamental technical component, we present a practical attribute-based sanitizable signature with shorter signature and key lengths and more efficient signing and signature-changing operations than the state-of-the-art policy-based sanitizable signature (P3S). Formal security analysis and experiments demonstrate the security and practicality of the proposed scheme for privacy-preserving healthcare sensing and medical data exchange. Full article

Deep learning has been widely deployed in critical scenarios such as the Internet of Things (IoT), industrial sensing, network intrusion detection, and cyber-physical system monitoring, where model inference directly affects system security, operational reliability, and service continuity. However, existing adversarial example detection studies remain insufficient for practical IoT deployment, as their validation often overlooks endpoint resource constraints, heterogeneous data modalities, physical environmental interference, communication protocol specifications, adaptive attacks, and adversary capability models. Moreover, detection outcomes are rarely connected with deployment locations, computational overhead, formal security assurance, and subsequent response strategies, which limits their engineering applicability. To address these limitations, this review systematically synthesizes recent representative studies in adversarial example detection and constructs a unified analytical framework integrating detection evidence, IoT deployment feasibility, and adaptive-attack evaluation. Based on the source of detection evidence, existing methods are categorized into input-consistency-based, feature-statistics-based, predictive-uncertainty-based, model-reconstruction-based, runtime-context-aware, and multi-strategy fusion detection, while formal certification is discussed as an independent security-assurance dimension. The review further analyzes the principles, applicable conditions, limitations, compatibility conflicts with IoT deployment constraints, and typical failure modes of these methods. The analysis identifies four key challenges: the lack of IoT-native adaptive evaluation, limited anomaly-boundary identification and cross-modal generalization, insufficient deployment-time security assurance, and weak coordination between detection decisions and security responses. Future research should therefore emphasize feasible attack paradigms, hierarchical lightweight detection, reliable multimodal fusion, certifiable operational boundaries, and auditable end-to-end response mechanisms, thereby supporting the evaluation and deployment of adversarial example detection in IoT scenarios. Full article

Gateway-resident intrusion detection can act before IoT traffic reaches cloud services, but early decisions are based on incomplete flow prefixes. This paper presents a reliability-aware edge–cloud framework that treats early detection as a sequential routing problem. At each checkpoint, a lightweight gated recurrent unit (GRU) maps causal packet-prefix features to a malicious-probability estimate. Temperature scaling, asymmetric benign and malicious thresholds, and an eight-packet minimum-evidence gate determine whether a flow exits locally, remains under observation, or is sent for cloud refinement. Short and unresolved flows are classified by regularized logistic regression using a compact 97-feature causal representation. The edge model contains 19,777 parameters, and each cloud submission carries 388 bytes of float32 features. The principal evaluation uses all 309 CIC-IoT-2023 PCAP files under four outer PCAP-disjoint folds, with separate edge-training, calibration, cloud-development, and final-test roles. Across 2,286,754 pooled out-of-fold flows with $88.54\%$ malicious prevalence, the framework resolves 422,190 flows at the edge and routes 1,864,564 for cloud refinement, reducing cloud submissions by $18.46\%$. The final policy attains $4.47\%$ FPR, $1.89\%$ FNR, $96.82\%$ balanced accuracy, and $98.76\%$ F1 score. Observation-budget analysis identifies 32 packets as a corpus-specific compromise, whereas controlled delays in post-eight-packet information expose the limits of short-prefix detection. On the balanced CICIDS2017 test set, in-domain development attains $97.03\%$ balanced accuracy; zero-shot transfer falls to $86.30\%$, and target-calibration-only adaptation improves it to $91.65\%$. Ablation results identify the minimum-evidence gate and cloud-refinement stage as the main reliability controls. Benign false alarms, delayed post-eight-packet information, cross-dataset shift, and scenario/file-level labels remain the principal limitations. Full article

Among the key computing challenges today are green computing and quantum computing, with the quantum paradigm introducing adverse consequences for many existing cryptographic protocols. However, the entire internet critically depends on these protocols. Therefore, building on theoretical preliminaries, this paper derives a coherent methodological basis for designing and developing quantum-computing-resistant and sustainable protocols for sensors and the Internet of Things (IoT) by extending traditional approaches and including large language models (LLMs). In line with these premises, this paper focuses on authenticated key exchange (AKE), which is one key security service. To support ever-growing needs under the described conditions and using the developed methodology (called the Sustainability-focused LLM-supported crypto protocols development process, SFLSCDP), this paper presents a novel, formally verified AKE protocol family. This family of protocols can be considered ultra-lightweight, as a corresponding metric is also given. Consequently, additional effective means for security services in the post-quantum computing era are provided, even for computationally weak devices. Full article

Outsourcing Internet-of-Things (IoT) data and computation to cloud and fog infrastructure exposes both the data and the access-control process to integrity, confidentiality, and privacy risks. Attribute-based encryption (ABE) provides fine-grained access control but, as deployed today, suffers from single-authority bottlenecks, expensive policy updates, weak auditability, and exposure to secret-key leakage, classical primitives are additionally threatened by future quantum adversaries. This paper does not propose a new cryptographic scheme. Instead, it contributes a conceptual reference architecture that systematizes how a set of existing, standardized primitives can be composed into a single access-control framework for IoT outsourcing, and it makes the resulting design precise enough to reason about. Concretely, we (i) define a system model and a threat model covering passive, active, colluding, bounded-leakage, and harvest-now-decrypt-later quantum adversaries; (ii) instantiate each layer with a named construction decentralized multi-authority ABE, attribute-based proxy re-encryption for policy updates, a bounded leakage resilient key model, ASCON lightweight AEAD, and ML-KEM/ML-DSA post-quantum primitives, together with a permissioned, on-chain digest/off-chain payload logging layer; (iii) specify the end-to-end data flow and module interfaces; and (iv) give a goal-by-goal security rationale and an analytical evaluation based only on standardized parameter sizes and asymptotic complexity. We are explicit about what is inherited from prior work, what remains to be proven for the composed system, and that a measured prototype evaluation remains future work. The intended value of this paper is to provide a clear, composable, and honestly scoped design that subsequent implementation studies can build upon. Full article

MQTT is widely used in Internet of Things (IoT) systems because of its lightweight publish–subscribe architecture and efficient support for resource-constrained devices. Although wildcard subscriptions simplify topic management, their impact on broker performance, stability, and security under authenticated high-load conditions has not been comprehensively investigated. Existing studies typically evaluate routing performance, security mechanisms, or broker scalability independently, leaving a limited understanding of their combined effects. This paper presents a systematic experimental evaluation of wildcard subscription behavior in an authenticated MQTT v5 environment. A controlled testbed employing TLS-based authentication and role-based access control was used to compare exact-topic subscriptions with single-level (+) and multi-level (#) wildcard subscriptions under progressively increasing workloads. Performance was evaluated using end-to-end latency, CPU utilization, throughput, delivery success rate, broker stability, and authorization exposure. The experimental results demonstrate that increasing wildcard-subscription complexity significantly increases routing overhead, resulting in higher latency and CPU utilization while reducing throughput and broker service capacity. Multi-level wildcard subscriptions consistently exhibited the greatest performance degradation and reached broker saturation at lower workload levels than exact-topic subscriptions, demonstrating that wildcard density compresses the broker’s operational stability region. The experiments also show that broad wildcard-based access control policies increase the risk of authorization leakage when improperly configured. These findings demonstrate that wildcard-subscription complexity is a critical determinant of MQTT scalability, broker stability, and security, and provide practical guidance for designing efficient and secure IoT messaging infrastructures. Full article

With the rapid development of quantum computing, in response to its disruptive threats to traditional cryptosystems and the urgent demand for lightweight and highly scalable secure group communication among resource-constrained devices in large-scale Internet of Things (IoT) scenarios, this paper proposes a lightweight anonymous group authentication scheme that integrates Physical Unclonable Functions (PUFs), distributed Gossip algorithms, and quantum key distribution. By exploiting the uniqueness and unclonability derived from the inherent physical characteristics of PUF hardware, the scheme fundamentally eliminates attack vectors against quantum computers without requiring devices to pre-store any secret keys in their memory, while the QKCS pre-provisions CRPs and key seeds, which is the standard enrollment procedure in PUF-based systems. Combined with information-theoretically secure quantum keys as session keys, it forms a dual protection mechanism: anti-forgery at the physical layer and anti-quantum attack at the cryptographic layer. Innovatively, the Gossip algorithm is deeply integrated with group key agreement, converting global broadcast into local iterative interactions between nodes, which effectively alleviates broadcast storms and improves the scalability and fault tolerance of the protocol. Meanwhile, a pseudonym mechanism is introduced to achieve anonymous identity protection, and a dynamic key update strategy guarantees forward and backward security when members join or leave the group. Formal verification based on BAN logic and security analysis show that the proposed protocol can resist typical attacks such as replay attacks, man-in-the-middle attacks, and impersonation attacks. Performance evaluations demonstrate that our scheme outperforms existing comparable schemes in terms of computational cost, communication overhead, and dynamic group management efficiency, demonstrating its potential for resource-constrained IoT environments, pending further validation on real hardware platforms. Full article

The rapid deployment of Internet of Things (IoT)-enabled video surveillance systems has expanded the capabilities of real-time monitoring in smart cities, healthcare facilities, industrial environments and critical infrastructure. However, integrating resource-constrained cameras, heterogeneous communication protocols, edge/cloud analytics, and sensitive video data creates a complex cybersecurity landscape. This systematic review synthesizes recent evidence on security challenges and mitigation strategies in IoT-enabled video surveillance systems. Following the PRISMA 2020 guidelines, four bibliographic databases (Scopus, IEEE Xplore, Web of Science, and Google Scholar) were searched for peer-reviewed journal articles and conference papers published between January 2021 and July 2025. After duplicate removal, title/abstract screening, full-text assessment, and quality appraisal, 21 studies were included for qualitative synthesis. The findings show that vulnerabilities occur across three interdependent architectural layers: device/perception, network/communication, and application/cloud. The frequently reported weaknesses were default credentials, insecure firmware, unencrypted video streams, weak protocol configuration, metadata leakage, and inadequate cloud access control. Existing mitigation strategies, including multi-factor authentication, role-based access control, TLS/DTLS, lightweight encryption, intrusion detection systems, and secure boot, provide partial protection but remain constrained by latency, computational overhead, energy consumption, scalability, cost and legacy device compatibility. This review further identifies a persistent research–practice gap: only a small subset of studies provides evidence of real-world deployments, while most solutions remain evaluated in simulations, testbeds, or conceptual frameworks. This review contributes a domain-specific taxonomy of IoT video surveillance security, a comparative evaluation of mitigation strategies using technical, operational, and economic criteria, and deployment-oriented recommendations for smart city, industrial, healthcare, residential, and critical infrastructure settings. The study highlights the need for cross-layer security architectures, lightweight and post-quantum-ready cryptography, privacy preservation, edge AI, federated learning, zero-trust access control, and standardized security baselines. Full article

Airdrops with autonomous ram-air type parachutes are increasingly important in today’s unconventional warfare conjuncture and humanitarian aid operations. However, there are fundamental issues to be considered by operators or decision makers as to its utilization in the theatre. It is inevitable that new threats will arise with the increase in technology. Therefore, cyber defense elements for air supply should be secured for guided parachute systems that have the ability to glide through long distances. Some of these implied cyber-attacks could target sensitive information (identity, location, etc.) carried by guided parachutes, which are basically unmanned aerial vehicles, and deviate the system by taking over the routing control. The capture of flight information could lead to the disclosure of such covert operations, or at least lead to unexpected complications such as unauthorized airspace violations. Due to various adverse situations that may occur, ensuring the cybersecurity of the parachute payload system both in flight and on the ground has always been an important research topic. In this study, the concept of information replenishment with autonomous parachute systems is introduced to the literature and the cybersecurity of the system is detailed. Specifically, an efficient, lightweight, and pairing-free Elliptic Curve Cryptography (ECC)-based authentication scheme is proposed to secure the system. Considering the resource-constrained nature of autonomous parachute platforms, the proposed scheme is designed to ensure robust security with minimal computational and communication overheads. Furthermore, a security evaluation of the proposed scheme is performed. Although ECC-based authentication protocols have been widely investigated for UAV and IoT systems, this study is, to the best of our knowledge, the first to adapt a lightweight authentication framework to the cybersecurity requirements of autonomous ram-air parachute systems. Full article

We present Dipper, a lightweight 64-bit block cipher with 96-bit and 128-bit key variants, built on a 28-round hybrid SPN–ARX structure. Each round applies a full-state key addition, sixteen parallel 4-bit GIFT S-boxes, four word-wise rotations, two 16-bit modular additions over half of the state, and the GIFT-64 bit permutation, combining the compact substitution layer of GIFT-style designs with the diffusion efficiency of ARX operations. We evaluate Dipper from both hardware and cryptanalytic perspectives under a single, fully open-source methodology. Round-based Verilog implementations were synthesized alongside PRESENT, GIFT, and SIMON variants using an identical Yosys + ABC + Nangate45 flow. Under this flow, Dipper-64/96 and Dipper-64/128 require 2498 and 2824 gate equivalents (GE), respectively, both falling between GIFT-64-128 (2191 GE) and PRESENT-128 (2963 GE); notably, Dipper-64/128 is more compact than PRESENT-128 at the same key size, despite incorporating an additional ARX diffusion layer. A broader comparison re-implements eleven established lightweight ciphers under the same flow, and post-place-and-route FPGA results on Lattice ECP5, measured software timings, and Cortex-M memory footprints support deployment across RFID, sensor-node, and edge-gateway scenarios. For differential resistance, we develop a mixed-integer linear programming (MILP) model that couples the exact GIFT differential distribution table with a Lipmaa–Moriai encoding of modular addition. Predicted and empirical differential probabilities agree tightly for reduced-round variants, while five-round trails reveal differential clustering. The security evaluation further includes proven-optimal linear trail bounds up to ten rounds, an exhaustive impossible-differential search bounding the longest distinguisher at five rounds, and experimental integral distinguishers of at most five rounds, leaving the 28-round cipher a margin close to 3× against the longest identified distinguisher. All RTL, synthesis scripts, reference implementations, and MILP models are released for full reproducibility. Full article

This paper proposes a trust-aware environmental state consensus framework for smart agriculture that integrates TEE-enabled sensing, Byzantine-resilient aggregation, and lightweight blockchain-based state coordination under resource-constrained IoT environments. Unlike conventional IoT systems that treat blockchain as a transactional ledger for directly storing sensor outputs, the proposed framework utilizes blockchain as a state commitment layer that records only validated environmental state transitions. In the proposed architecture, distributed sensor readings are modeled as noisy and potentially adversarial observations of an underlying physical state rather than directly trusted measurements. To establish a reliable trust boundary between physical sensing and distributed coordination, TEE-enabled sensing devices provide authenticated and integrity-protected data outputs before blockchain processing. The TEE component is adopted as a deployed trusted execution anchor rather than a newly designed hardware security mechanism, and its role is to protect sensing-side execution and provide trustworthy inputs for subsequent coordination. Since trusted execution alone cannot guarantee the correctness of sensor observations, a Byzantine-resilient aggregation mechanism is introduced to estimate consistent environmental states under faulty or adversarial sensing conditions. The validated states are then committed through a lightweight permissioned blockchain to provide tamper-evident state finality using a K-confirmation-based commitment mechanism. The proposed framework is implemented and evaluated on a real greenhouse IoT platform with distributed sensing nodes and edge computing devices. Experimental results demonstrate that the proposed approach improves environmental state consistency under varying adversarial conditions while maintaining stable blockchain coordination and resource-aware execution performance. Full article

The rapid deployment of sixth-generation (6G) Internet of Things (IoT) networks demands lightweight authentication mechanisms that provide low latency, high scalability, and robust security for resource-constrained devices operating in dynamic wireless environments. Conventional authentication approaches based on Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS), blockchain-assisted architectures, and Generic Bootstrapping Architecture (GBA)-based schemes introduce significant computational complexity, communication overhead, synchronization delays, and infrastructure dependencies, limiting their suitability for large-scale edge-assisted IoT environments. This paper proposes Lightweight Timed Efficient Stream Loss-Tolerant Authentication over the Constrained Application Protocol (L-TESLA-CoAP), a lightweight and infrastructure-independent authentication framework that integrates adaptive TESLA delayed-key authentication, CoAP communication, edge-assisted synchronization, replay-aware synchronization, SHA3-HMAC-based symmetric authentication, and rotating pseudonym identities to provide continuous packet-level authentication. The proposed framework was implemented and evaluated using a Python-based simulation environment under constrained 6G IoT communication scenarios with network sizes ranging from 50 to 1000 IoT devices. The comparative evaluation against CoAP, DTLS, TLS, Blockchain-CoAP, and GBA-Hybrid TESLA shows that the proposed framework achieves low authentication latency (approximately 0.8–1.3 s) and low energy consumption (approximately 60–75 mJ) while maintaining packet-loss recovery capability, reduced communication overhead, reduced computation time, low memory consumption, and authentication throughput. Furthermore, the proposed framework provides resilience against replay, packet injection, impersonation, synchronization manipulation, and denial-of-service attacks through adaptive synchronization and delayed key disclosure. These results indicate that L-TESLA-CoAP provides an efficient, scalable, and lightweight authentication solution suitable for next-generation edge-assisted 6G IoT applications. Full article

Precision crop production increasingly relies on Internet of Things (IoT) devices, heterogeneous sensor networks, machine telemetry, and data-intensive analytics to monitor field conditions, support decisions, and enable variable-rate or autonomous operations. However, farm-scale multi-season adoption remains limited by technical constraints that are often reported only as general challenges. This design-oriented review clarifies those constraints at the infrastructure level. It is not a quantitative meta-analysis; rather, it combines a transparent multi-database search, a PRISMA-type selection record and thematic design synthesis of the 2020–2025 literature, supplemented by selected 2026 studies and current interoperability and security specifications. The review addresses four research questions covering field hardware and connectivity failures, edge-to-cloud data management, integration with farm management information systems and agricultural machinery, and future design priorities. The synthesis identifies recurring gaps in multi-season reliability evidence, calibration and self-diagnostics, energy and connectivity benchmarking, operational definitions of agricultural Big Data, metadata/FAIR implementation, ISO 11783/ISOBUS–FMIS interoperability, lightweight cybersecurity, and serviceability. The main outputs are an evidence-traceability matrix, a distributed reference architecture specifying inputs, outputs, standards, validation points and edge/cloud placement, an operationalized G1–G10 gap matrix with indicators and evaluation designs, and minimum reporting requirements for future agricultural IoT studies. These outputs are intended to make field systems more interoperable, maintainable, secure, and evaluable. Because the corpus combines heterogeneous evidence types and does not support quantitative meta-analysis, the outputs should be interpreted as design and reporting guidance rather than comparative performance estimates. Full article

With the widespread deployment of IoT devices, deploying efficient network traffic classification models on resource-constrained edge nodes is critical for real-time boundary security. However, traditional lightweight models primarily rely on macro-level structural pruning, which often sacrifices crucial feature extraction capabilities when handling complex heterogeneous traffic, leading to a severe imbalance between parameter compression and detection accuracy. To overcome this bottleneck, we propose TinyFlowNet, an ultra-lightweight multi-module fusion architecture. To prevent the parameter explosion inherent in combining CNN, LSTM, and Transformer modules, TinyFlowNet innovatively adopts an extreme operator-level reconstruction strategy. By introducing debiased computations, affine-free normalization, and a customized micro-self-attention mechanism, it comprehensively strips away underlying redundant parameters. Simultaneously, an integrated parameter-free regularization mechanism is introduced to compensate for the representational capacity lost under this extreme compression, ensuring robust spatio-temporal feature fusion. Comprehensive evaluations on the custom X-IDS-20 balanced dataset alongside the complex CICDarknet2020 and ToN_IoT public datasets demonstrate that TinyFlowNet achieves exceptional accuracies of 95.31 percent, 99.53 percent, and 97.13 percent, respectively. Furthermore, it exhibits formidable robustness against extreme class imbalances by securing a peak Matthews Correlation Coefficient of 0.9465 and an outstanding PR-AUC of 0.9834, all while strictly confining the parameter count to merely 74,600. Crucially, actual on-device hardware profiling on a commercial edge device corroborates its deployment viability, exhibiting a minimal dynamic memory footprint of 8.26 MB, an average inference latency of 0.79 ms, and a processing throughput exceeding 1200 FPS. Compared to a standard heavy Hybrid CNN-LSTM-Transformer baseline, TinyFlowNet achieves superior detection accuracy while drastically reducing the parameter footprint by over 99.3% and computational FLOPs by 95.8%. Furthermore, against mainstream lightweight benchmarks like DistilBERT and heavy baselines such as LSTM, TinyFlowNet reduces parameters by 61.4% to 94% while simultaneously achieving absolute accuracy leaps and accelerating inference speed by nearly 4× over MobileNetV2, establishing a highly efficient new paradigm for intelligent edge defense. Full article

Mobile Radio Frequency Identification (RFID) systems are emerging as a fundamental part of modern smart environments, enabling automatic identification, tracking, and data exchange among different mobile platforms. While these systems are increasingly being adopted, they have a major drawback: an RFID tag has very little computational power, and the wireless communication channels can be attacked by adversaries. Several authentication and key management mechanisms to protect data and provide secure access have been proposed to solve these problems. In this study, we propose a new scheme that improves system security through explicit three-party mutual authentication, epoch-based pseudonym indexing for $O\left(1\right)$ server lookup, and comprehensive resiliency against replay, impersonation, and man-in-the-middle attacks. An in-depth security analysis, along with performance evaluation, substantiates that the proposed protocol improves privacy and resilience without losing compatibility with low-cost RFID tags equipped only to perform lightweight cryptographic functions. This protocol also provides epoch-based unlinkability and is well suited for large-scale deployments, as found in healthcare, logistics, and Internet of Things (IoT) applications. Full article