Preliminary Results of Applying Machine Learning Algorithms to Android Malware Detection
Matthew Leeds, Travis Atkison · 2016
As the use of mobile devices continues to increase, so does the need for sophisticated malware detection algorithms. The preliminary research presented in this paper focuses on examining permission requests made by Android apps as a means for detecting malware. By using a machine learning algorithm, we are able to differentiate between benign and malicious apps. The model presented achieved a classification accuracy between 75% and 80% for our dataset and the best combination of parameters. Future work will seek to improve the model by expanding the training dataset, taking more features into account, and exploring other machine learning algorithms.