Ransomware has emerged as one of the most pervasive and destructive threats in the realm of cybersecurity, targeting individuals, businesses, and institutions globally. Traditional antivirus solutions often fall short in detecting sophisticated ransomware variants due to their reliance on signature-based approaches. This study proposes an automated ransomware detection and classification framework leveraging supervised machine learning models. The framework extracts key features from network traffic and file behaviors to train models capable of accurately distinguishing ransomware from benign software. Comparative analysis of algorithms such as Random Forest, Support Vector Machines, and Gradient Boosting highlights their performance in terms of accuracy, precision, recall, and F1-score. Results demonstrate that machine learning significantly enhances the detection and classification of ransomware, offering real-time solutions for mitigating this cyber threat. The proposed system is poised to contribute to more robust and adaptive cybersecurity strategies in combating ransomware attacks.

The increasing demand for sustainable construction materials has driven the exploration of supplementary cementitious materials (SCMs) to enhance the properties of concrete while reducing its environmental impact. This study investigates the synergistic effects of glass powder (GP) and rice husk ash (RHA) as partial replacements for cement in concrete. Various mix proportions were tested to evaluate their influence on mechanical properties, including compressive strength, tensile strength, and flexural strength, as well as durability parameters such as water absorption, chloride resistance, and sulfate attack.

Results reveal that the combination of GP and RHA significantly improves the mechanical properties of concrete, particularly at later ages, due to the pozzolanic reactions and filler effects. The inclusion of these materials also enhances durability by reducing permeability and improving resistance to chemical attacks. Optimal performance was observed at a combined replacement level of 20–30%, beyond which a slight reduction in strength was noted. This research highlights the potential of using GP and RHA to produce eco-friendly concrete with improved performance, aligning with sustainability goals in the construction industry.