Detecting front-running attacks in Ethereum blockchain transactions is crucial for maintaining security and integrity within decentralized ecosystems. However, existing models struggle to accurately model the complex distributions inherent in tabular data, particularly in the presence of class imbalance and mode collapse. This paper leverages the potentials of Conditional Tabular Generative Adversarial Networks and PacGAN, called a Conditional Packing GAN (cPacGAN), to address these challenges. cPacGAN effectively generates synthetic data that closely mimics the distribution of real transactions, thereby augmenting the dataset and improving the performance of front-running attack detection. PacGAN mitigates mode collapse by incorporating packed samples in the discriminator, improving the diversity of generated samples and improving the stability of the training process. Through experimental evaluations of a real-world Ethereum transactions dataset, cPacGAN demonstrates improved performance across all selected machine learning classifiers, particularly augmenting the effectiveness of Tabular Neural Networks (TabNet).

Calibration in wireless sensor networks (WSNs) poses a significant challenge, particularly in uncontrolled environmental deployments for environmental monitoring, such as air pollution. Traditional calibration methods rely on centralized reference stations, which are costly to maintain, offer limited coverage, and calculate measurements as averages. However, with the rise of the Internet of Things (IoT), sensors present a cost-effective alternative for calibration compared to fixed reference stations. Nevertheless, in uncontrolled environments, sensors require self-recalibration to ensure accurate measurements for the reliable operation of WSNs without human intervention. Existing calibration approaches, such as LSTM, are computationally expensive, have higher memory requirements, and exhibit training instability, making them unsuitable for resource-constrained WSNs. This paper proposes a self-calibration approach for WSNs using the Gated Recurrent Unit (GRU) coupled with the attention mechanism (Attention-GRU). The Attention-GRU selectively focuses on relevant features while capturing long-term dependencies, akin to Recurrent Neural Networks (RNNs), thereby mitigating overfitting. Experimental results demonstrate that the Attention-GRU model outperforms other models with an R-squared value of 0.97 and accelerated learning. These accurate sensor recalibration predictions promote sustainability by supporting IoT-enabled air pollution monitoring efforts.