Ransomware of the Advanced Persistent Threat (APT) type are very sophisticated and often have a contingency plan of attack in case they are discovered while the attack is in progress. Due to the ever-changing trait of such APT-type ransomware, an intelligent and robust intrusion detection system (IDS) is the need of the hour and in this paper, we put forward machine learning (ML) and natural language processing (NLP) based intrusion detection systems. We utilize a commercial simulator to run different real-world ransomware attacks to create, for the first time, a dataset for APT-type ransomware research. Then, we develop multiple IDSes by training ML models like support vector machine (SVM), logistic regression (LR), gradient boosting (GB) decision trees, random forest (RF), naive Bayes classifier (NBC), and an NLP model called BERT, on this dataset. With our intelligent IDS, we could precisely distinguish the system calls of processes spawned by ransomware from legitimate system calls. We compare the different intrusion detection systems developed using the six aforementioned models. The IDS using the NLP BERT model achieves the best accuracy of 99.98%, and the IDS using the Naive Bayes Classifier achieves an accuracy of 98.55%. Furthermore, we discuss the tradeoffs of these models for designing an intelligent IDS. The advancement in cyber attacks, especially ransomware-based attacks, necessitates this upgrade in IDS which is essential for a strong defense.