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Ransomware has appeared as one of the major global threats in recent days.The alarming increasing rate of ransomware attacks and new ransomware variantsintrigue the researchers to constantly examine the distinguishing traits ofransomware and refine their detection strategies. Application ProgrammingInterface (API) is a way for one program to collaborate with another; API callsare the medium by which they communicate. Ransomware uses this strategy tointeract with the OS and makes a significantly higher number of calls indifferent sequences to ask for taking action. This research work utilizes thefrequencies of different API calls to detect and classify ransomware families.First, a Web-Crawler is developed to automate collecting the Windows PortableExecutable (PE) files of 15 different ransomware families. By extractingdifferent frequencies of 68 API calls, we develop our dataset in the firstphase of the two-phase feature engineering process. After selecting the mostsignificant features in the second phase of the feature engineering process, wedeploy six Supervised Machine Learning models: Na"ive Bayes, LogisticRegression, Random Forest, Stochastic Gradient Descent, K-Nearest Neighbor, andSupport Vector Machine. Then, the performances of all the classifiers arecompared to select the best model. The results reveal that Logistic Regressioncan efficiently classify ransomware into their corresponding families securing99.15% overall accuracy. Finally, instead of relying on the 'Black box'characteristic of the Machine Learning models, we present the post-hoc analysisof our best-performing model using 'SHapley Additive exPlanations' or SHAPvalues to ascertain the transparency and trustworthiness of the model'sprediction.

Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis