This study presents a novel malware detection framework for cloud infrastructures that harnesses the power of Convolutional Neural Networks (CNNs) to achieve real-time threat identification with superior accuracy and speed. Our approach begins with the collection and meticulous preprocessing of heterogeneous cloud log data, followed by advanced feature engineering to extract meaningful patterns indicative of malicious activity. The CNN model automatically learns hierarchical representations from this high-dimensional data, resulting in a detection system that achieves an accuracy of 98.2%, a precision of 97.5%, a recall of 98.0%, and an F1-score of 97.8%. In addition, the model operates with a low latency of 12 ms, a critical factor for timely threat mitigation in dynamic cloud environments. Comparative analysis against Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Random Forest classifiers reveals that the CNN not only outperforms these models in key performance metrics but also maintains a significant advantage in processing speed. These findings highlight the potential of CNN-based approaches to enhance cybersecurity defenses, offering a scalable and efficient solution for detecting evolving malware threats in cloud infrastructures.

This paper presents a real option approach to the valuation of the default risk of residential mortgages. Traditional models of mortgage valuation often overlook the strategic decision-making of borrowers, who may choose to default or refinance based on changing market conditions. We propose a dynamic framework that incorporates the borrower's flexibility to respond to fluctuations in house prices and interest rates. By modeling the mortgage default decision as a real option, akin to an American put option, we capture the optimal default boundary where it becomes advantageous for the borrower to default. Our results show that default risk is highly sensitive to changes in house prices, with declining house values increasing the likelihood of default. Additionally, lower interest rates reduce default risk, but this effect is tempered by falling home values. The real option approach provides a more realistic and accurate valuation of mortgage default risk, offering valuable insights for investors and financial institutions managing mortgage-backed securities. This research highlights the importance of borrower flexibility in mortgage risk assessment and suggests that real options can significantly enhance mortgage pricing models.