Malware Detection of Android Based Systems using SIMGRU

Abstract

To put it simply, malware is posing a serious threat to global network security as the Internet era has progressed at an unprecedented rate. Using SIMGRU, we present an Android malware detection method that falls under the static detection category. Using the clustering similarity, which is widely used in static analysis of Android malware, we were able to improve the Gated Recurrent Unit (GRU) and produce three distinct structures of Sim GRU: Input Sim GRU, Hidden Sim GRU, and Input hidden SIMGNU There are two types of input hidden sim gru: input and hidden. Results show that the GRU model and other methods are outperformed by the Sim GRU, Hidden GRU, and Input Hidden GRU.