Traditional intrusion detection systems (IDS) based on packet signatures are widely used in network security but often fail to detect previously unseen attacks. To overcome this limitation, machine learning-based methods have been explored to identify anomalous patterns in network traffic indicative of unknown intrusions. In this study, we propose an IDS model based on the Long Short-Term Memory Autoencoder (LSTM-AE), specifically a Convolutional Neural Network Bidirectional LSTM Autoencoder (CNN-BiLSTM-AE). The model integrates convolutional layers for spatial feature extraction and bidirectional LSTM layers to capture temporal dependencies in both directions. By leveraging CNNs to extract key spatial features and BiLSTM to model sequential patterns, the proposed architecture enables effective differentiation between normal and malicious traffic. Anomalies are detected by computing reconstruction loss during inference and applying a predefined threshold to classify traffic. The experimental results demonstrate that the CNN-BiLSTM-AE model achieves high detection performance, with an accuracy of 98.1% and an F1-score of 98.3%, highlighting its effectiveness in identifying previously unknown intrusions.

Monitoring the occurrence of plant diseases and pests such as fungi, viruses, nematodes, and insects in crops and collecting environmental information such as temperature, humidity, and light levels is crucial for sustainable greenhouse management. It is essential to control the environment through measures like adjusting vents, using shade nets, and employing screen controls to achieve optimal growing conditions, ensuring the sustainability of the greenhouse. In this paper, an artificial intelligence-based integrated environmental control system was developed to enhance the sustainability of the greenhouse. The system automatically acquires images of crop diseases and augments the disease image information according to environmental data, utilizing deep-learning models for classification and feedback. Specifically, the data are augmented by measuring scattered light within the greenhouse, compensating for potential losses in the images due to variations in light intensity. This augmentation addresses recognition issues stemming from data imbalances. Classifying the data is done using the Faster R-CNN model, followed by a comparison of the accuracy results. This comparison enables feedback for accurate image loss correction based on reflectance, ultimately improving recognition rates. The empirical experimental results demonstrated a 94% accuracy in classifying diseases, showcasing a high level of accuracy in real greenhouse conditions. This indicates the potential utility of employing optimal pest control strategies for greenhouse management. In contrast to the predominant direction of most existing research, which focuses on simply utilizing extensive learning and resources to enhance networks and optimize loss functions, this study demonstrated the performance improvement effects of the model by analyzing video preprocessing and augmented data based on environmental information. Through such efforts, attention should be directed towards quality improvement using information rather than relying on massive data collection and learning. This approach allows the acquisition of optimal pest control timing and methods for different types of plant diseases and pests, even in underdeveloped greenhouse environments, without the assistance of greenhouse experts, using minimal resources. The implementation of such a system will result in a reduction in labor for greenhouse management, a decrease in pesticide usage, and an improvement in productivity.

The continuously evolving cyber threat landscape necessitates not only resilient defense mechanisms but also the sustained capacity development of security personnel. However, conventional training pipelines are predominantly dependent on static real-world datasets, which fail to adequately reflect the diversity and dynamics of emerging attack tactics. To address these limitations, this study employs a Wasserstein GAN with Gradient Penalty (WGAN-GP) to synthesize realistic network traffic that preserves both temporal and statistical characteristics. Using the CIC-IDS-2017 dataset, which encompasses diverse attack scenarios including brute-force, Heartbleed, botnet, DoS/DDoS, web, and infiltration attacks, two training methodologies are proposed. The first trains a single conditional WGAN-GP on the entire dataset to capture the global distribution. The second employs multiple generators tailored to individual attack types, while sharing a discriminator pretrained on the complete traffic set, thereby ensuring consistent decision boundaries across classes. The quality of the generated traffic was evaluated using a Train on Synthetic, Test on Real (TSTR) protocol with LSTM and Random Forest classifiers, along with distribution similarity measures in the embedding space. The proposed approach achieved a classification accuracy of 97.88% and a Fréchet Inception Distance (FID) score of 3.05, surpassing baseline methods by more than one percentage point. These results demonstrate that the proposed synthetic traffic generation strategy provides advantages in scalability, diversity, and privacy, thereby enriching cyber range training scenarios and supporting the development of adaptive intrusion detection systems that generalize more effectively to evolving threats.

Network Intrusion Detection Systems (NIDS) play a crucial role in protecting network environments against cyberattacks. However, traditional NIDS rely heavily on predefined attack signatures, which limits their ability to detect zero-day attacks. Although machine learning-based intrusion detection techniques have been widely adopted in Network Intrusion Prevention Systems (NIPS), publicly available network traffic datasets often suffer from severe class imbalance, leading to biased learning and degraded detection performance. To address this issue, this study proposes data augmentation framework based on a 3D-GAN (Three-Discriminator Generative Adversarial Network). The proposed architecture integrates an autoencoder, a CNN (Convolutional Neural Network), and an LSTM (Long Short-Term Memory) network as parallel discriminators to capture the statistical, spatial, and temporal characteristics of network traffic. By jointly optimizing multiple discriminator losses, the framework enhances training stability and generates high-quality synthetic samples. Experiments were conducted on the CIC-UNSW-NB15 dataset using Random Forest-, XGBoost (eXtreme Gradient Boosting)-, and BiGRU (Bidirectional Gated Recurrent Unit)-based classifiers. Two augmented datasets were constructed to address class imbalance, containing approximately 100,000 and 350,000 samples, respectively. Among them, Dataset 2, augmented using the proposed 3D-GAN, demonstrated the most significant performance improvement. Compared to the original imbalanced dataset, the XGBoost classifier trained on Dataset 2 achieved approximately a 4% increase in both accuracy and F1-score, while reducing the false positive rate and false negative rate by approximately 3.5%. Furthermore, the optimal configuration attained an F1-score of 0.9816, indicating superior capability in modeling complex network traffic patterns. Overall, this study highlights the potential of GAN-based data augmentation for alleviating class imbalance and improving the robustness and generalization of intrusion detection systems.

EEG-based emotion recognition results may be artificially inflated due to data leakage from normalization, augmentation, or validation splits. This article introduces SGTA-Net, a Subject-Aware Graph-Temporal Attention Network, evaluated on the DEAP dataset using only EEG features. The approach incorporates feature-space baseline removal, two-stage subject- and fold-aware normalization, window-wise graph convolution over electrode positions, and bidirectional temporal learning with additive attention. Three models—an autoencoder (AE), an AE with graph neural network layers (AE-GNN), and SGTA-Net—are compared under stratified and subject-group five-fold cross-validation for four-class and binary valence/arousal tasks. The primary comparison is the macro-F1 difference between SGTA-Net and the plain AE under identical task and protocol conditions. For four-class group-aware validation, SGTA-Net achieves 73.11% compared to 68.06% for AE (+5.05 percentage points), while binary group-aware differences are minimal (−0.03 points for valence and +0.13 points for arousal). As a secondary robustness analysis, SGTA-Net demonstrates the smallest stratified-minus-group-aware macro-F1 gap (5.33 points for four-class, compared to 15.06 for AE and 12.48 for AE-GNN). A three-seed component ablation reveals that removing the BiLSTM reduces macro-F1 by 2.01 points (significant across subjects but not across folds after Holm correction), whereas removing attention or the graph layers does not result in significant loss. These findings support the importance of window-level temporal modeling and leakage-controlled, protocol-explicit EEG benchmarking.