id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_ayatblpinzaztdzhilw6yqsi6m	Sana Aurangzeb	On the classification of Microsoft-Windows ransomware using hardware profile	2021	24	.pdf	application/pdf	11016	1378	55	Keywords Malware, Ransomware, Performance counters, Classification, Machine learning Most contemporary state-of-theart dynamic analysis techniques detect and classify ransomware that hides itself applications that uses machine learning to classify malicious behavior of malware based previously studied for the analysis and detection of ransomware applications. HPCs to detect Microsoft Windows-based ransomware by analyzing the execution hardware related performance features are extracted from the data set of 160 malware 2019; Victoriano, 2019) related to Android malware detection using machine learning ransomware classification and proposed a learning-based detection strategy. and dynamic or runtime features of executing applications to detect ransomware. HPCs features to detect malware from benign applications. (2016) has presented a machine learning-based malware analysis have used hardware performance counters to detect Android malware, and in another (using signature-based features and hardware performance counters) to detect and classify performance counters-based malware detection. Automatic ransomware detection and analysis based on Anatomy of ransomware malware: detection, analysis and	./cache/work_ayatblpinzaztdzhilw6yqsi6m.pdf	./txt/work_ayatblpinzaztdzhilw6yqsi6m.txt