Karanam, Sanjula2023-06-012023-06-012023-05-31vt_gsexam:37322http://hdl.handle.net/10919/115272Ransomware is an ever-growing issue that has been affecting individuals and corporations since its inception, leading to losses of the order of billions each year. This research builds upon the existing body of research pertaining to ransomware detection for Windows-based platforms through behavioral analysis using sandboxing techniques and classification using machine learning (ML), considering the various predefined function calls, known as API (Application Programming Interface) calls, made by ransomware and benign samples as classifying features. The primary aim of this research is to study the effect of the frequency of API calls made by ransomware samples spanning across a large number of ransomware families exhibiting varied behavior, and benign samples on the classification accuracy of various ML algorithms. Conducting an experiment based on this, a quantitative analysis of the ML classification algorithms was performed, for the frequency of API calls based input and binary input based on the existence of an API call, resulting in the conclusion that considering the frequency of API calls marginally improves the ransomware recall rate. The secondary research question posed by this research aims to justify the ML classification of ransomware by conducting behavioral analysis of ransomware and goodware in the context of the API calls that had a major effect on the classification of ransomware. This research was able to provide meaningful insights into the runtime behavior of ransomware and goodware, and how such behavior including API calls and their frequencies were in line with the MLbased classification of ransomware.ETDenIn CopyrightRansomware DetectionBehavioral AnalysisWindows OSAPI CallsAPI Call FrequencyMachine LearningSandboxingFeature ImportanceThesis