This paper proposes an applied framework for cyberattack and anomaly detection in resource-constrained embedded/IoT environments by combining signal-processing feature construction with supervised and unsupervised AI (Artificial Intelligence) models. The workflow covers dataset preparation and normalization, correlation-driven feature analysis, and compact representations via PCA (Principal Component Analysis), followed by classification and anomaly scoring. In addition to the original UNSW-NB15 (University of New South Wales—Network-Based Dataset 2015) traffic features, Fourier-domain descriptors, wavelet-domain descriptors, and Kalman-based smoothing/innovation features are considered to improve robustness under variability and measurement noise. Detection performance is assessed using classical and ensemble learning methods (SVM (Support Vector Machines), RF (Random Forest), XGBoost (Extreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine)), unsupervised baselines (K-Means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise)), and DL (Deep-Learning) anomaly detectors based on Autoencoder reconstruction and GAN (Generative Adversarial Network)-based scoring. Experimental results on UNSW-NB15 indicate that ensemble-based models provide the strongest overall detection performance, while the signal-processing augmentation and PCA-based compactness support efficient deployment in embedded contexts. The findings confirm that integrating lightweight signal processing with AI-driven models enables effective and adaptable identification of malicious network traffic supporting deployment-oriented embedded cybersecurity and motivating future real-time validation on edge hardware.

Fire detection is a critical task for early warning systems, particularly in environments where visual sensing is unreliable. While most existing approaches rely on image-based or smoke-based detection, acoustic signals provide complementary information capable of capturing early combustion-related events. This study investigates deep learning models for sound-based fire detection, focusing on convolutional and Transformer-based architectures. VGG16 and VGG19 convolutional neural networks are adapted to process time-frequency audio representations for binary classification into Fire and No-Fire classes. An Audio Spectrogram Transformer (AST) is further employed to model long-range temporal dependencies in acoustic data. Finally, a hybrid VGG19-AST architecture is proposed, in which convolutional layers extract local spectral–temporal features, and Transformer-based self-attention performs global sequence modeling. The models are evaluated on a curated dataset containing fire sounds and diverse environmental background noises under multiple noise conditions. Experimental results demonstrate competitive performance across convolutional and Transformer-based models, while the proposed hybrid VGG19-AST architecture achieves the most consistent overall results. The findings suggest that integrating convolutional feature extraction with self-attention-based global modeling enhances robustness under complex acoustic variability. The proposed hybrid framework provides a scalable and cost-effective solution for sound-based fire detection, particularly in scenarios where visual monitoring may be obstructed or ineffective.

This paper proposes a methodological framework for building a binary audio dataset for the automatic classification of fire sounds and forest ambience. Two operational recordings, one for the fire class and one for the forest class, are used strictly as seed data for controlled segmentation and augmentation. The workflow includes mono conversion at 16 kHz, amplitude normalization, segmentation into 5 s windows with 2 s overlap, low-intensity stochastic augmentation, and the systematic logging of the generated samples. The study also explains why augmented data are appropriate for training and internal validation, while final performance claims must remain reserved for testing on independent, standardized real recordings.

Embedded voice-command systems are increasingly adopted in cyber-physical and industrial environments, yet their machine-learning pipelines remain vulnerable to data poisoning attacks that can silently degrade recognition performance or induce targeted misclassifications. This paper proposes a multi-stage poisoning detection pipeline tailored for embedded voice-command applications, combining fast integrity screening with progressively stronger detection models. First, an audio integrity layer analyzes waveform and spectrogram consistency to flag abnormal patterns indicative of injected perturbations or corrupted samples. Second, a lightweight machine-learning detector based on Random Forests operates on compact statistical descriptors extracted from spectrogram representations, enabling efficient on-device or edge-level screening. Third, a spectrogram-based Convolutional Neural Network provides high-sensitivity discrimination between clean and poisoned samples, acting as a robust validation stage in higher-assurance deployments. To increase resilience, the training procedure integrates regularization and controlled augmentation to mitigate overfitting to spurious artifacts and reduce poisoning sensitivity. The proposed approach is designed to be deployable under embedded constraints by separating low-cost screening from deeper analysis, and it offers a structured methodology for securing voice-command pipelines against poisoning threats.