Ransomware Clustering and Classification using Similarity Matrix

Ransomware Detection Using Machine Learning: A Survey

“…Machine-learning-based detection is a more advanced approach that relies on training a machine learning model to detect ransomware based on its behavior patterns or features. This approach is based on collecting a large dataset of benign and malicious samples, extracting relevant features from them, and then training a machine learning model to classify new samples as peaceful or hostile based on their characteristics [32,33].…”

“…This approach is based on creating a database of known ransomware signatures or marks and scanning the system or network for matching signatures or patterns. If a match is found, the ransomware is flagged as malicious and appropriate actions are taken [32,33].…”

“…This approach is based on creating rules or heuristics that describe typical ransomware behavior and then monitoring the system or network for any deviations or anomalies from these rules. If such variations or abnormalities are detected, the ransomware is flagged as suspicious or malicious, and appropriate actions are taken [32,33].…”

“…Network-based detection is an approach that relies on monitoring the network traffic for suspicious or malicious activity that may be indicative of a ransomware attack. This approach is based on analyzing the network traffic for anomalies or patterns characteristic of ransomware, such as large volumes of outbound traffic, unusual network connections, or network traffic encryption [32,33].…”

“…Hybrid detection is an approach that combines different ransomware-detection techniques to improve the overall detection accuracy and speed. This approach combines the strengths of other detection techniques, such as signature-based, heuristic-based, machinelearning-based, and network-based detection, to create a more robust and effective detection system [32,33].…”