A ML Framework For DGA-Malware Detection

Abstract: Malware has always been a threat to the computer world, but with fast growth in the use of the Internet, malware severely affects the computer world. Malware predictors and detectors are critical tools in defense against malware. The existing malware detectors and predictors have been created, the effectiveness of these detectors and predictors depend upon the techniques being used. This study specifically, addressed the following objectives. Real-time detection of domain names that are generated using the domain generating algorithms (DGA) is a challenging cyber security challenge. DGAs can constantly generate large amounts of domains to evade blacklist detection. Traditional malware control methods, such as blacklisting, are insufficient to handle DGA threats. In-order to solve this problem we decided to use machine learning algorithms to detect DGA domains and compare the performance of these algorithms. In this research project, we first performed feature engineering. Then applied preprocessed data to machine learning models like a random forest, LSTM, logistic regression. Keywords-Blacklisting, Malware, domain generation algorithm (DGA), machine learning, security, networking