The rapid expansion of Internet of Things (IoT) technologies has significantly increased the digital attack surface, exposing modern networks to sophisticated cyber threats, particularly zero-day attacks that exploit previously undisclosed vulnerabilities. Conventional intrusion detection systems (IDSs), especially signature-based approaches, rely heavily on predefined attack patterns and large labeled datasets, which limits their effectiveness in identifying emerging and previously unseen attacks. To address this limitation, meta-learning has recently emerged as a promising paradigm for enabling intrusion detection under data-scarce conditions. However, the comparative evaluation of gradient-based and metric-based meta-learning approaches remains relatively underexplored in the domain of intrusion detection. In this study, the potential of meta-learning for zero-day intrusion detection is explored through the evaluation of two representative strategies within a few-shot learning framework: a gradient-based approach based on Model-Agnostic Meta-Learning (MAML), which enables rapid adaptation to new attack types, and a metric-based approach using Prototypical Networks, in which classification is performed within a learned embedding space. For additional comparative analysis, a Siamese network-based Fully Connected Network (FC-Net) is implemented as a baseline model. The framework evaluation is conducted using three diverse and realistic benchmark datasets, including CICIDS2017, CICIoT2023, and an augmented CIC-UNSW-NB15 dataset. Zero-day attack scenarios are simulated under multiclass classification settings to reflect practical deployment environments. Experimental results demonstrate that the MAML-based model consistently achieves superior performance across all datasets, obtaining 96.67% accuracy and 97.54% recall on CICIDS2017, 92.87% accuracy and 92.99% recall on CICIoT2023, and 83.20% accuracy and 83.50% recall on CIC-UNSW-NB15. These findings highlight the effectiveness of gradient-based meta-learning for rapid adaptation to previously unseen attacks and demonstrate its potential for developing intelligent IDSs capable of addressing evolving zero-day threats across heterogeneous network environments.
With the worsening environment quality credited to rapid urbanization and industrialization, ensuring optimal indoor air quality is a critical challenge with far-reaching implications for public health, environmental sustainability, and overall wellbeing. Despite its importance, real-time air quality monitoring solutions (AQMS) remain underdeveloped, leaving significant gaps in the ability to analyze and predict environment quality effectively. In this paper, we have presented the development of a Machine Learning Internet of Things (ML-IoT) enabled AQMS equipped with diverse sensors capable of sensing vital environmental parameters which include carbon monoxide, carbon dioxide, particulate matter PM2.5, PM10, temperature, and humidity. We deployed the developed AQMS in an indoor environment and collected environmental data from sensors. A comprehensive preprocessing pipeline, including outlier removal using the interquartile range method and feature scaling, was applied to improve data quality. Three machine learning models including Linear Regression (LR), Random Forest (RF), and Bidirectional Long Short-Term Memory (BiLSTM) were implemented for predictive analysis. Using state-of-the-art data analytics, we have tested these ML models to uncover trends, identify correlations, and predict air quality metrics with improved accuracy. To ensure methodological rigor, 5-fold cross-validation was applied to LR and RF models, while time-series cross-validation was used for BiLSTM to preserve temporal dependencies. The results show that LR achieves high accuracy for temperature prediction with
R
2
of 96.45% and cross validation
R
2
of 95.4%, while RF provides moderate performance for CO
2
prediction with
R
2
of 52.7% and 64.05% in cross validation. BiLSTM improves CO
2
prediction with
R
2
of 89.9%) under standard evaluation and achieves an average
R
2
of 85.4% under time-series validation, demonstrating generalization. To provide real-time visualization of air quality parameters, we have designed a user-friendly dashboard, allowing stakeholders to monitor real-time conditions and derive actionable insights. The developed AQMS finds promising application in industries, houses, office and residential buildings, allowing predictive environment quality monitoring and triggering alarm in case of any anomaly.
Tanzila, Sundus Ali, M. Aslam et al.· Frontiers in Environmental S...· 0 citations
The proposed AgriX-SENet framework effectively combines high classification performance with model interpretability, addressing a key limitation of existing CNN-based plant disease detection systems and making it a promising solution for scalable agricultural diagnostics.
S. Raj, Prashant Johri, Vishwadeepak Singh Baghela et al.· Frontiers in Plant Science· 1 citation
A lightweight, explainable IDS that combines a 1D-CNN for spatial feature analysis with SHAP for model interpretation, yielding streamlined models that preserve over 93% F1-score and reduce computational overhead by more than 38%, facilitating millisecond-level inference on edge hardware.
Miracle Udurume, Vladimir V. Shakhov, Insoo Koo· Scientific Reports· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
