In the context of the increasing scale and complexity of disasters caused by rapid climate change, a comprehensive understanding of disaster big data is essential for effective detection and response. The disaster knowledge graph proposed in this paper fills this gap by capturing the connections between various disaster-related data sources and their potential for growth across heterogeneous datasets. We generate time-series disaster graphs every minute using SNS data (e.g., Twitter) and public data, specifically focusing on disasters. Then, we create disaster knowledge graphs to represent the relationships between various data sources and try to predict their potential developments. We label and annotate knowledge graphs and then detect sudden changes in time-series disaster knowledge graphs for disaster detection. To that end, we assess the effectiveness of three state-of-the-art GNN models for graph-based event classification using Graph Convolutional Network (GCN), Graph Attention Network (GAT), and SageConv. In addition, we evaluate a simple clustering model, K-means, for comparison. Our experiments show promising results with approximately 87% precision in detecting disaster events using structural data and connectivity patterns within disaster graphs. Finally, we measure the result of disaster detection time with an unseen dataset, showing positive results that about 70% detect a disaster in less than 3 minutes. To comprehensively analyze real-time social media data and understand the patterns of disaster to enhance disaster management and response strategies, our approach combines the strength of GNNs with a designed disaster knowledge graph.

The explosive rise of malware poses risks to Android developers and organization regarding security lapses and monetary losses. The dynamic nature, changing complexity and behavior over time, and increasing velocity and volume make it challenging for the malware protection community to provide a robust and reliable protection system. Due to these characteristics, conventional Android malware detection techniques, such as signature-based and battery-monitoring, cannot detect futuristic malware. Current research exploiting deep learning methods shows excellent performance compared to conventional and machine learning methods. However, the majority of the techniques are proposed for only binary classification. These classification models are tested on customized datasets. They do not provide the model’s effectiveness in terms of generalization, as the model’s accuracy might be good for some malware classes. Hence, providing a practical, robust, stable, and reliable malware model is still an open issue. Therefore, in this work, we propose an Attention-based deep learning model to detect categorical malware classes. The attention-based deep learning mechanism learns the malicious behavior of target classes. The attention mechanism filters and extracts the relevant information more effectively by focusing on the specific keywords in a target sample.

In recent years, a low-cost micro-electro-mechanical systems (MEMS) acceleration sensor has been widely used for earthquake early warning (EEW). In our previous work, we introduced a networked earthquake detection system, CrowdQuake with three-hundred smartphones’ acceleration sensors and a deep-learning based earthquake detection model. For one year’s operation, CrowdQuake detected a series of earthquakes and collected various earthquake and non-earthquake data. Based on the successful operation of CrowdQuake, in this paper, we discuss how it can be expanded across the country by addressing the following challenges: (1) sensor deployments for highly dense network, (2) earthquake detection performance using a deep learning model, and (3) high performance and scalable system design for big data processing. The improved system is CrowdQuake+ which can deal with acceleration data sent from 8,000 IoT sensors and detect an earthquake in few seconds using a newly proposed detection model. Moreover, CrowdQuake+ stores all acceleration data sent from sensors and assesses their qualities by calculating noise levels. Then, the collected data are used for deep learning model training, so that its detection performance becomes more accurate.