Ransomware Detection and Classification using Machine Learning
Kavitha Kunku ANK Zaman Kaushik Roy
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.