A DEEP LEARNING MODEL FOR DETECTING RANSOMWARE THROUGH BEHAVIORAL ANALYSIS

The increasing prevalence of ransomware attacks has emerged as a critical concern for modern computing environments, leading to significant financial damage and compromise of sensitive data for both individuals and organizations. Conventional security approaches, such as signature-based and rule-based detection systems, are often inadequate in identifying newly emerging ransomware variants due to their reliance on predefined patterns. To overcome these challenges, this study presents a deep learning-driven framework for ransomware detection that focuses on learning malicious behavior directly from system activity data. The proposed approach examines behavioral indicators including file system interactions, process activities, and system call sequences to effectively differentiate between legitimate and ransomware operations. Advanced deep learning techniques are utilized to model complex and non-linear relationships within the data, enabling the system to detect both known threats and previously unseen ransomware families. The model is trained and validated using real-world datasets that capture diverse system behaviors under normal and malicious conditions. Experimental findings indicate that the proposed method delivers high detection accuracy, minimizes false positives, and demonstrates superior generalization when compared to traditional machine learning approaches. This work emphasizes the potential of deep learning in developing intelligent, adaptive, and scalable cybersecurity solutions, making it highly suitable for real-time ransomware detection in practical applications.