Ransomware Analysis using Feature Engineering and Deep Neural Networks
This work addresses ransomware detection for cybersecurity, but it is incremental as it applies existing methods to new data with specific feature insights.
The paper tackled ransomware detection by identifying key features like Registry changes, API calls, and DLLs through feature engineering, and used machine learning and deep neural networks to achieve detection, with datasets of up to 3646 samples.
Detection and analysis of a potential malware specifically, used for ransom is a challenging task. Recently, intruders are utilizing advanced cryptographic techniques to get hold of digital assets and then demand a 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 the 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 the N-Gram technique. It is also observed that in the 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 files. And thus this discernible fact can be readily exploited for Ransomware detection.