Search Results (225)

Environmentally released carbon nanomaterials can alter polycyclic aromatic hydrocarbon (PAH) attenuation in agricultural soils, yet how their properties influence biodegradation in association with contaminant bioavailability and indigenous microbial responses remains unclear. This study compared nano-biochar (NBC), pristine and hydroxylated multi-walled carbon nanotubes (MWCNTs; 4–6 nm and >50 nm) in phenanthrene-contaminated soil, using raw biochar (RBC) as parent reference material. NBC showed the strongest promotion, reaching 68.17% biodegradation at 60 d and a maximum rate of 3.00 mg/kg/d, compared with 58.31% and 2.00 mg/kg/d in the unamended phenanthrene-spiked control. RBC exerted a weaker positive effect, suggesting biochar nanosizing favored biodegradation, accompanied by moderate bioavailability reduction and stimulation of microbial abundance, enzyme activity and diversity. In contrast, MWCNTs inhibited biodegradation, with reduced β-HPCD-extractable phenanthrene, suppressed polyphenol oxidase activity and more pronounced community shifts, while higher abundance of PAH-degradation gene nidA did not correspond to enhanced biodegradation. Pristine MWCNTs showed the lowest initial β-HPCD-extractable phenanthrene and biodegradation rates, whereas small-diameter MWCNTs most strongly limited final biodegradation extent, consistent with greater surface reactivity and intensified cell-contact stress. Hydroxylated MWCNTs exhibited weaker inhibition than pristine counterparts. These findings highlight the importance of material properties in shaping PAH fate and microbial ecology in agricultural soils. Full article

This paper presents the Adaptive Confidence-Gated Ensemble (ACGE) framework for Network Intrusion Detection Systems (NIDSs) in resource-heterogeneous fog-IoT deployments. No single algorithmic paradigm simultaneously achieves high minority-class detection accuracy, sub-millisecond inference latency, and generalization across heterogeneous IoT traffic at resource-constrained fog-IoT nodes. To address this challenge, ACGE integrates a Sequence Transformer Encoder with LightGBM via a learned and sample-adaptive gating network trained on inter-model error complementarity, formally specified within a three-mode tiered deployment architecture. Both models execute in parallel for every sample; the gating network assigns transformer-dominant weight (α ≈ 1) to 18% of samples where attention-based processing yields the greatest architectural advantage. Under true parallel inference, ACGE latency is 1.45 ms (bounded by the transformer); the measured wall-clock latency under CPU-GPU pipeline parallelism is 1.52 ms. The framework is evaluated under 5-fold stratified cross-validation with McNemar’s significance testing on IoTID20 (primary IoT benchmark) and NSL-KDD (reproducibility anchor) and validated through zero-shot transfer on CICIoT2023 and Edge-IIoTset. Notably, the Sequence Transformer Encoder and ACGE process L = 5 consecutive flow records per decision, providing five times the information available to single-flow baseline models; approximately 57% of the transformer’s performance advantage is attributable to architectural design rather than extended input access. On IoTID20 multiclass classification, ACGE is the only evaluated configuration yielding a negative accuracy-to-macro-F1 gap (−0.51 pp), reaching 99.12 ± 0.03% accuracy and 98.67 ± 0.08% balanced accuracy, demonstrating genuine minority-class improvement. LightGBM achieves the optimal fog-tier profile (0.97 ms latency, concept drift sensitivity of −0.53 pp over three months). On Edge-IIoTset zero-shot transfer, ACGE achieves 93.89% binary accuracy and 85.78% multiclass macro-F1. Ransomware detection (12.34–66.34% F1 across all models) is identified as the primary capability gap; targeted data collection, few-shot adaptation, and federated learning are recommended as the most critical future directions. All inter-paradigm performance differences are reported as the mean ± SD and verified by McNemar’s test with Bonferroni correction. Full article

Class imbalance, where majority-class samples vastly outnumber minority-class samples, remains a persistent challenge in network intrusion detection systems (NIDS), often causing classifiers to overlook rare but critical attack types while yielding misleadingly optimistic performance metrics. Synthetic data generation is a common mitigation strategy. However, existing methods often fail to capture the non-linear network traffic and neglect inter-class relationships with the majority class, resulting in inconsistent performance gains. This study investigates three contrastive learning loss functions, Contrastive Loss, Soft Nearest Neighbor Loss, and Supervised Contrastive Loss, integrated into a Conditional Variational Autoencoder (CVAE) regularised via the standard Kullback-Leibler divergence objective. Experiments were conducted on four widely used NIDS benchmark datasets (NSL-KDD, UNSW-NB15, CIC-IDS2017, and CSE-CIC-IDS2018), with synthetic data evaluated using four machine learning classifiers against the original imbalanced data, a non-contrastive CVAE, and conventional oversampling approaches. The results show that the effectiveness of integrating contrastive learning into the CVAE framework is dependent on the specific dataset, minority class, contrastive loss function, and distance metric, with the proposed approach outperforming traditional oversampling techniques in several settings without degrading majority-class performance or overall accuracy. These findings provide practical guidance for selecting contrastive learning objectives in class-imbalanced NIDS scenarios. Full article

Mango (Mangifera indica L.), particularly the ‘Nam Dok Mai’ cultivar, is an economically important fruit crop in Thailand and is widely produced in the Lower Northern Region for domestic consumption and export. However, pesticide residues in mango remain a major food safety concern because of their potential effects on consumer health and compliance with national and international maximum residue limits (MRLs). This study investigated pesticide residue contamination and assessed the dietary health risks associated with mango consumption among different age groups. A total of 255 composite mango samples were collected from production areas in Phichit, Phitsanulok, and Phetchabun provinces, Thailand. Pesticide residues were analyzed using a validated modified QuEChERS extraction method combined with gas chromatography–tandem mass spectrometry (GC–MS/MS). Multiple pesticide residues were frequently detected in the samples analyzed. Of the detected residues, 22 pesticides exceeded Thai MRL standards and 14 exceeded Codex Alimentarius MRLs. Dietary exposure was evaluated using acute and chronic hazard quotients (HQa and HQc) and cumulative hazard indices (HIa and HIc). The results showed that acute exposure to several insecticides, particularly carbofuran, lambda-cyhalothrin, and fenpropathrin, exceeded acceptable risk thresholds in all age groups. At the pesticide-class level, cumulative acute exposure to organophosphates, carbamates, and pyrethroids showed HIa values greater than 1 in several age groups, suggesting potential short-term health concerns, especially among children. In contrast, chronic exposure to individual pesticides and cumulative pesticide groups remained below the level of concern for all age groups, with HQc and HIc values below 1. These findings indicate that although long-term dietary risk from mango consumption is within acceptable levels, acute exposure to certain pesticide residues may pose non-negligible health risks. Continuous residue monitoring, stricter enforcement of good agricultural practices, and evidence-based risk management are essential to ensure mango safety and protect consumers. Full article

Software-Defined Networking (SDN) separates the control and data planes, introducing a logically centralized controller that is itself a high-value attack target. Despite growing interest in SDN intrusion detection, publicly available datasets either restrict evaluation to binary normal-vs-DDoS classification or lack control-plane telemetry, leaving multi-class detection of SDN-architectural attacks without a dedicated benchmark. This work presents LAN-SDN-NIDS, a publicly available, multi-class flow-level dataset of 1,125,059 records generated in a fully containerized Containernet/OpenDaylight testbed across five standard network topologies. Each flow record combines 29 traffic-level features with 11 control-plane-aware metrics—including Packet-In and Flow-Mod counts and first-seen delay. The dataset covers five attack classes in two categories: three that exploit SDN control-plane mechanisms (link fabrication, host injection, and port hijack) alongside DDoS and port scan, plus normal traffic. An XGBoost classifier trained on the full feature set achieved a macro F1 of 0.94; an ablation study showed that removing OpenFlow features causes link fabrication F1 to collapse from 0.97 to 0.19, indicating that control-plane telemetry is decisive for detecting SDN-architectural attacks under the conditions evaluated. A UMAP embedding is consistent with class separability, except for a structural overlap between host injection and normal traffic attributable to their shared ARP protocol. Full article

Aging river levees are heterogeneous flood-defense earthworks whose reliability is affected by limited site data, interparameter dependence, and spatial variability. This study develops an integrated framework combining normal information diffusion (NID), Copula-based dependence identification, cross-correlated random fields (CCRFs), and Monte Carlo reliability analysis. Reliability was evaluated using Monte Carlo simulation based on a simplified infinite-slope benchmark model. Data were obtained from the authors’ own site investigations and laboratory tests. NID yields the lowest Kolmogorov–Smirnov statistics for all documented variables and better preserves local fluctuations and lower-strength tails than four classical marginals. Gaussian Copula is preferred for the representative bivariate cases and slightly outperforms the multivariate t Copula for Hongpaihe. For strongly dependent Beiwei data, the correlated model gives a mean factor of safety of 1.235 and a failure probability of 0.153, whereas independence gives a similar mean factor of safety of 1.238 but increases failure probability to 0.200. The weakly dependent main-dike case changes negligibly, while the φ–γ pair dominates the trivariate response. Illustrative CCRF realizations show how pointwise dependence can propagate into spatially continuous weak zones. Failure probability is therefore more diagnostic than mean factor of safety when stochastic model form is uncertain. The main novelty lies in the consistent integration and propagation of marginal, dependence, and spatial uncertainties within a unified levee reliability framework. Full article

Adipose tissue development significantly influences meat quality and economic traits in beef cattle. In this study, we identified a novel circular RNA derived from two exons (exons 3 and exons 9) of the NID2 gene, designated as circNID2, which exhibits high structural stability and marked upregulation during bovine adipocyte differentiation. Functional assays demonstrated that circNID2 suppresses the proliferation and apoptosis of bovine preadipocytes while robustly promoting adipogenic differentiation and lipid accumulation. Although containing several putative open reading frames, circNID2 lacks protein-coding potential. Mechanistically, circNID2 functions as a competing endogenous RNA by sponging miR-339a, thereby relieving the post-transcriptional repression of its downstream target, Neuronatin (NNAT). Functional rescue experiments further validated that circNID2 partially neutralizes the regulatory effects of miR-339a on preadipocyte proliferation, apoptosis, and differentiation. Collectively, these findings demonstrate that circNID2 regulates bovine preadipocyte function through the newly established circNID2/miR-339a/NNAT axis. This study provides novel insights into the post-transcriptional mechanisms governing mammalian adipogenesis and highlights circNID2 as a potential molecular target for improving fat deposition traits in cattle. Full article

Machine learning techniques for network-based intrusion detection systems (NIDS) have advanced considerably over the past decade. Still, improvements are inhibited by handcrafted feature pipelines, isolated public benchmark data, and evaluation procedures that do not reflect real-life deployment. AutoML, a branch of ML automating model selection, automated architecture search, and the creation of model pipelines, may help overcome these shortcomings. While numerous NIDS applications employing automated ML techniques have been proposed, and recent surveys have mapped the AutoML framework landscape for network intrusion detection, no existing review critically audits the evaluation practice of this literature: the quality of its benchmark datasets, the reproducibility of its reported results, and the realism of its deployment assumptions. This paper critically reviews 26 research works published between January 2023 and June 2026, collected via a two-phase structured search: a documented keyword search across five databases (Scopus, IEEE Xplore, Web of Science, ACM Digital Library, and Google Scholar), followed by full-text eligibility screening, citation chaining, and expert evaluation. Findings drawn from this collection capture trends observed among the selected studies, rather than reflecting the broader state of the field. Analysis of the corpus reveals that 88% of dataset-verified studies evaluate exclusively or partly on the legacy benchmark family (KDD-derived, CICIDS, UNSW-NB15, CIDDS), 21% evaluate on a single dataset only, and among attribute-verified studies only 32% release source code, 40% report statistical significance testing, and 36% include variance analysis, findings that collectively motivate the four contributions of this study. First, a recommended evaluation framework is proposed, addressing baseline parity, transparent search-space and budget reporting, nested cross-validation for selection-bias control, and stability reporting across multiple random seeds. Second, a dataset quality scoring framework is introduced, assessing five dimensions: overlap rate, duplication rate, label correctness, attack-type representativeness, and coverage of benign, IoT, and IIoT traffic. Third, a cross-domain justification is provided for neural architecture search (NAS) and meta-learning in NIDS, grounded in advances in federated NAS, out-of-distribution robustness, edge-constrained search cost reduction, and few-shot adaptation. Fourth, a structured research roadmap is outlined, targeting real-world validation, standardized benchmarks, curated datasets, resource-aware AutoML, and privacy-preserving federated NAS. In contrast to prior surveys of AutoML for network intrusion detection, which map frameworks and computational paradigms, this review contributes a formalized evaluation checklist, an explicit and partially empirically validated dataset quality scoring scheme, and evidence-based methodological guidance grounded in a transparent, fully enumerated study corpus. Full article

Network Intrusion Detection Systems (NIDS) face a trade-off between detection accuracy and computing efficiency, particularly in the edge environment with strict real-time requirements and limited resources. Current approaches rely on complicated models, which are computationally demanding, or simple ones, which sacrifice detection performance. To deal with this issue, we propose a lightweight cloud-edge cooperative two-stage NIDS architecture, which separates the real-time detection and detailed classification. At the edge, a decision tree based on feature selection is used for rapid binary classification by using only the top 10 most informative features, thus efficiently screening out abnormal traffic with minimum processing cost. Meanwhile, the cloud server identifies attack classification accurately by using a hybrid CNN-BiLSTM-Attention model to capture the spatial structures, temporal relationships, and semantic relevance. This hierarchical design effectively balances detection performance and system efficiency. Experiments conducted on UNSW-NB15, NSL-KDD, and CIC-IDS2017 datasets indicate that our suggested scheme can obtain competitive performance both at the edge and in the cloud. The edge model obtains binary classification accuracy of 86.04%, 95.27%, and 99.01%, respectively, with very low processing cost (less than 100 FLOPs per sample). The cloud model achieves multi-class accuracy of 92.23%, 97.18%, and 98.66%, respectively, with AUC values higher than 0.98. The hierarchical cloud–edge collaborative design provides an efficient and accurate solution for intrusion detection under resource-restricted situations. Full article

IPv4 fragmentation attacks, including tiny fragment injection, teardrop offset manipulation, and fragment flooding exploit the RFC 791 reassembly process to evade firewalls and network intrusion detection systems (NIDSs). Detection is challenging because widely used flow-level datasets (UNSW-NB15, CIC-IDS2017), lack the packet-level fragment header information required for direct RFC 791 validation. This study investigates whether fragmentation-related traffic can be identified using only flow-level statistical features. The proposed framework introduces four contributions: (1) a direction-corrected proxy labeling scheme, where flows are labeled as fragmented when ${CV}_L$ < 0.70, validated on a controlled 3000-flow Scapy dataset with $F_1$ = 0.873 and ROC-AUC = 0.895 against verified packet-level ground truth; (2) a dual-model Random Forest architecture with feature separation to prevent circular self-prediction; (3) RFC 791-inspired statistical heuristics applied as a post-inference filter; and (4) a six-configuration ablation study with a reproducible protocol. The study distinguishes fragmentation-like statistical signatures from confirmed packet-level fragmentation. The benchmark model $M_{frag}$ achieves $F_1$ ≈ 0.998 on synthetic data, while external PCAP validation yields $F_1$ = 0.873. Unlike most flow-level NIDS research, the framework is validated against both controlled Scapy-generated traffic and the MAWI real-world backbone trace, establishing a practical performance bound ($F_1$ = 0.82 for the full hybrid framework). The attack classification model $M_{attack}$ achieves $F_1$ = 0.974 on dataset-provided labels. Results show that flow-level analysis can provide useful indicators of fragmentation-related activity when packet-level evidence is unavailable, while highlighting the limitations of statistical detection. Full article

Real-world IoT network security generates traffic at big-data scale with extreme class imbalance, temporal non-stationarity, and continuously evolving attack strategies that overwhelm static supervised classifiers. This paper presents a cognitive computing framework for network intrusion detection: a CNN–LSTM–DQN architecture with Prioritized Experience Replay (PER) evaluated on a 5,000,000-flow naturalistic sample of the TON_IoT Processed_Network dataset (4,000,000 training/1,000,000 temporally held-out test flows; 94.5% attack ratio) under a strict temporal split. The cognitive agent optimizes detection decisions using an Alerts per Million Flows (ARMF)-aware reward function that encodes both alert-fatigue cost and missed-attack penalty. We conduct a cross-attack-family generalization study: the methodology—architecture template, reward design, and hyperparameter calibration—is inherited from a framework previously validated on CSE-CIC-IDS2018, re-instantiated and retrained on the structurally different TON_IoT environment, and compared against the previously published benchmark. Initialization sensitivity is characterized across five independent random seeds using paired Wilcoxon signed-rank and t-tests. Across the five seeds, the proposed X2 model attains recall 0.833 ± 0.306 and F1 0.874 ± 0.241 (mean ± sample SD), versus the supervised X1 baseline at 0.858 ± 0.178 and 0.912 ± 0.116; the best-performing seed (42) achieves 97.52% accuracy, 98.02% attack recall, 99.46% precision, and 98.73% F1-score on 1,000,000 held-out XSS flows—an attack family entirely absent from training—with temporal stability variances of 4.63 × 10⁻⁷ (recall) and 1.38 × 10⁻⁷ (F1). The X2 advantage observed among the four stable seeds is not statistically demonstrated at n = 5 (statistical power ≈ 5.1%); the initialization-sensitivity finding itself, including one degenerate alert-suppression seed, is reported as a primary contribution. A formal, exactly additive ARMF decomposition distinguishes the detected-attack (structural) component (99.46%) from the model-induced false-positive component (0.54%), and we report a multi-seed, ARMF-aware cognitive IDS evaluation on naturalistic TON_IoT traffic under an unseen-attack-family test condition that, to the best of our knowledge, has not been reported in the surveyed RL-based NIDS literature. Full article

Background: Despite significant advances in identifying genetic risk factors for multiple sclerosis (MS), the causal involvement of circulating proteins in disease development and severity remains largely unexplored. Understanding these protein-mediated pathways could reveal novel therapeutic opportunities for this complex neurological disorder. Methods: We performed a proteome-wide Mendelian randomization study integrating data from four large-scale protein quantitative trait loci studies to investigate causal relationships between plasma proteins and both MS susceptibility and severity. Results: Our analysis revealed four plasma proteins with significant causal effects on MS susceptibility: ALPI and MXRA8 were associated with increased risk, while IDUA and NID2 showed protective effects. For disease severity, MANSC1 and LYVE1 were associated with worse disability, whereas CBR1 demonstrated protective effects. Conclusions: Our study provides genetic evidence supporting associations between specific plasma proteins and MS susceptibility and severity, implicating biological pathways related to gut–brain axis signaling, extracellular matrix remodeling, and oxidative stress. These findings improve our understanding of MS biology and identify candidate proteins for future functional validation, therapeutic investigation, and biomarker research. Full article

The rapid advancement of enterprise and cloud networks has significantly increased the complexity and scale of cyber threats, making conventional signature-based Intrusion Detection Systems (IDSs) insufficient. This paper presents the concept of an Intelligent Hybrid-Inference Network Intrusion Detection System (IHI-NIDS) powered by AI for real-time threat detection in high-velocity enterprise and cloud environments. IHI-NIDS combines Gated Recurrent Units (GRUs) for temporal sequence modelling with a multi-head self-attention mechanism, accounting for both short-range packet dynamics and long-range dependencies that signal multi-stage attacks. The system supports hybrid datasets that combine enterprise packet flows (PCAP/NetFlow) and cloud telemetry (VPC Flow Logs). It uses strong scaling and dimensionality reduction via PCA as the main approach to guarantee computational efficiency. Assessment of a composite dataset of 1.2 million flows indicates a detection rate of 97.8, recall of 96.1, F1-score of 96.5, and false alarm rate of 2.1, validating the capability to detect both enterprise-level and cloud-based threats. SHAP-based explainability highlights key features that affect predictions, which security analysts can use. Based on latency measurements, it can be deployed in real time to high-throughput networks, whereas temporal generalisation tests demonstrate its ability to withstand unobserved attack sequences. This article makes IHI-NIDS a scalable, interpretable, high-performance solution for current hybrid network security, filling the gap between operational viability and predictive AI-based defence. Full article

Network Intrusion Detection (NID) is essential for cybersecurity because network traffic and security threats are growing rapidly. However, current NID methods suffer from a critical limitation: they face inherent difficulties in jointly modeling global contextual dependencies and local fine-grained temporal features within network traffic, which leads to suboptimal detection accuracy. To address this, this paper proposes the Global and Local Temporal (GLT) NID model. This model utilizes the self-attention mechanism of the Transformer to extract global dependencies and complex feature correlations from network traffic sequences while combining the bidirectional recurrent structure of BiLSTM to capture local temporal features of traffic data. Additionally, to address the class imbalance issue in intrusion detection datasets, this paper introduces a power-smoothed soft weighting strategy for optimization. We tested the GLT model on the NSL-KDD and UNSW-NB15 datasets. On the NSL-KDD dataset, the model achieved 99.57% accuracy and 0.32% false positive rate. On the UNSW-NB15 dataset, it achieved 88.04% accuracy and 0.76% false positive rate. Compared to current state-of-the-art techniques, it demonstrates higher detection accuracy and lower false positive rates. Full article

The proliferation of digital infrastructures and the Internet of Things (IoT) has led to a rapid increase in interconnected devices, exposing modern systems to increasingly sophisticated cyber threats. Intrusion detection in such environments remains a major challenge due to limited device resources, evolving attack vectors, and diverse traffic patterns. Traditional systems often fall short in scalability and adaptability when facing these modern threats. This paper introduces MM-NIDS, a novel multimodal fusion framework for NetFlow-based intrusion detection. The framework combines four complementary NetFlow-derived data representations (numerical, textual, graph-based, and quantum-inspired), each modeled using transformer-based architectures, including FT-Transformer and ELECTRA-Small. Feature embeddings are constructed using robust engineering techniques, while predictions from the four base models are integrated through five post hoc fusion strategies: averaging-based fusion, weighted averaging, confidence-based fusion, and two meta-fusion methods based on a Multi-Layer Perceptron (MLP) and Extreme Gradient Boosting (XGBoost). Extensive cross-dataset evaluations on four public NetFlow-based benchmarks confirm the system’s robustness, with the text-based model (M2) consistently achieving the highest individual performance. Fusion approaches provided modest and dataset-dependent improvements in detection balance, especially for underrepresented attacks. A detectability hypothesis was proposed and validated, showing that NetFlow features are particularly effective for volumetric and scan-based attacks but less so for stealthy, payload-driven threats. These findings highlight the potential of MM-NIDS for deployment in critical infrastructure, industrial IoT, and smart environments, suggesting that future work should incorporate deeper semantic or payload-level features to enhance the detection of evasive threats further. Full article