Mobile Botnet Detection Using Convolutional Neural Networks(CNN)

Computer engineering

Botnets have been a serious threat to the Internet security. With their constant sophistication and the resilience of them, a new trend has emerged, shifting botnets from the traditional desktop to the mobile environment. As in the desktop domain, detecting mobile botnets is essential to minimize the threat that they impose. Along the diverse set of strategies applied to detect these botnets, the ones that show the best and most generalized results involved is- covering patterns in their anomalous behavior. In the mobile botnet field, one way to detect these patterns is by analyzing the operation parameters of this kind of application. In this paper, we present an anomaly-based and host-based approach to detect mobile botnets. The proposed approach uses machine learning algorithms to identify anomalous behaviors in statistical features extracted from system calls. We were able to test the performance of our approach in a close-to reality scenario. The proposed approach achieved great results, including low false positive rates and high true detection rates. Index Terms—Machine learning, Deep learning, Convolutional Neural Networks (CNN), Botnet Detection, etc.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.