Intelligent Machine Learning Framework for Advanced Cyber Threat Detection: A Focus on Ransomware Attacks

Ransomware has evolved into one of the most sophisticated cyber threats, causing severe financial and operational disruptions across healthcare, banking, education, government, and critical infrastructure sectors. Traditional signaturebased antivirus systems and rule-based intrusion detection mechanisms struggle to identify novel ransomware variants due to their rapidly evolving attack strategies. This paper proposes an Intelligent Machine Learning Framework (IMLF) for advanced ransomware detection that integrates supervised machine learning, feature engineering, behavioral analytics, and ensemble learning techniques to achieve real-time threat detection. The framework utilizes network traffic features, file system activities, registry modifications, process execution patterns, and API call sequences to distinguish ransomware from benign software. Multiple machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Deep Neural Networks (DNN) are evaluated using benchmark cybersecurity datasets. Experimental results demonstrate that ensemble-based approaches significantly outperform conventional methods, achieving detection accuracy above 99%, reduced false positives, and lower detection latency. The proposed framework provides an adaptive, scalable, and intelligent solution capable of detecting both known and zero-day ransomware attacks.