Ransomware Analysis using Feature Engineering and Deep Neural Networks
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Detection and Analysis of a potential malware specifically, used for ransom is a challenging task. Recently, intruders are utilizing advance cryptographic techniques to get hold of digital assets and then demand ransom. It is believed that generally, the files comprise of some attributes, states, and patterns that can be recognized by a machine learning technique. This work thus focuses on detection of Ransomware by performing feature engineering, which helps in analyzing vital attributes and behaviors of the malware. The main contribution of this work is the identification of important and distinct characteristics of Ransomware that can help in detecting them. Finally, based on the selected features, both conventional machine learning techniques and Transfer Learning based Deep Convolutional Neural Networks have been used to detect Ransomware. In order to perform feature engineering and analysis, two separate datasets (static and dynamic) were generated. The static dataset has 3646 samples (1700 Ransomware and 1946 Goodware). On the other hand, the dynamic dataset comprised of 3444 samples (1455 Ransomware and 1989 Goodware). Through various experiments, it is observed that the Registry changes, API calls, and DLLs are the most important features for Ransomware detection. Additionally, important sequences are found with the help of N Gram technique. It is also observed that in case of Registry Delete operation, if a malicious file tries to delete registries, it follows a specific and repeated sequence. However for the benign file, it doesnt follow any specific sequence or repetition. Similarly, an interesting observation made through this study is that there is no common Registry deleted sequence between malicious and benign file. And thus this discernible fact can be readily exploited for Ransomware detection. The relevant Python code and dataset are available at github.
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