Ransomware Entities Classification with Supervised Learning for Informal Text

Analyzing text especially in Malware domain is quite challenging. Even with Natural Language Processing approach, it limits with the absence of specific Named Entity recognizer to extract related entities of malware from unstructured data like text. It is essential to automate the process of extracting information such as Ransomware entity from text and the information extracted could be used as knowledge reasoning like profiling the behaviour of Ransomware using the information available on the Internet. Although the text itself is unstructured, informal text like Internet forum has its problems and challenges to perform the analysis and extraction process. Thus, the performance of machine learning in carrying out the classification of entities from this type of text depending on the complexity of the model. Therefore, this paper presents the comparison of few supervised learning techniques (CRF, Naive Bayes, and SVM) for model training in extracting Ransomware entities from unstructured text in terms of their performance.