Neural Networks Optimizations Against Concept and Data Drift in Malware Detection

Despite the promising results of machine learning models in malware detection, they face the problem of concept drift due to malware constant evolution. This leads to a decline in performance over time, as the data distribution of the new files differs from the training one, requiring regular model update. In this work, we propose a model-agnostic protocol to improve a baseline neural network to handle with the drift problem. We show the importance of feature reduction and training with the most recent validation set possible, and propose a loss function named Drift-Resilient Binary Cross-Entropy, an improvement to the classical Binary Cross-Entropy more effective against drift. We train our model on the EMBER dataset (2018) and evaluate it on a dataset of recent malicious files, collected between 2020 and 2023. Our improved model shows promising results, detecting 15.2% more malware than a baseline model.

Fusion of Satellite Images and Weather Data with Transformer Networks for Downy Mildew Disease Detection

Crop diseases significantly affect the quantity and quality of agricultural production. In a context where the goal of precision agriculture is to minimize or even avoid the use of pesticides, weather and remote sensing data with deep learning can play a pivotal role in detecting crop diseases, allowing localized treatment of crops. However, combining heterogeneous data such as weather and images remains a hot topic and challenging task. Recent developments in transformer architectures have shown the possibility of fusion of data from different domains, for instance text-image. The current trend is to custom only one transformer to create a multimodal fusion model. Conversely, we propose a new approach to realize data fusion using three transformers. In this paper, we first solved the missing satellite images problem, by interpolating them with a ConvLSTM model. Then, proposed a multimodal fusion architecture that jointly learns to process visual and weather information. The architecture is built from three main components, a Vision Transformer and two transformer-encoders, allowing to fuse both image and weather modalities. The results of the proposed method are promising achieving 97\% overall accuracy.