Ransomware Detection and Classification using Machine Learning
This addresses cybersecurity threats from ransomware for industries and businesses, but it is incremental as it uses existing methods on a specific dataset.
This study tackled the problem of ransomware detection and classification by applying XGBoost and Random Forest algorithms to analyze ransomware behavior and extract features, achieving high accuracy in detecting and classifying ransomware attacks.
Vicious assaults, malware, and various ransomware pose a cybersecurity threat, causing considerable damage to computer structures, servers, and mobile and web apps across various industries and businesses. These safety concerns are important and must be addressed immediately. Ransomware detection and classification are critical for guaranteeing rapid reaction and prevention. This study uses the XGBoost classifier and Random Forest (RF) algorithms to detect and classify ransomware attacks. This approach involves analyzing the behaviour of ransomware and extracting relevant features that can help distinguish between different ransomware families. The models are evaluated on a dataset of ransomware attacks and demonstrate their effectiveness in accurately detecting and classifying ransomware. The results show that the XGBoost classifier, Random Forest Classifiers, can effectively detect and classify different ransomware attacks with high accuracy, thereby providing a valuable tool for enhancing cybersecurity.