PICAndro: Packet InspeCtion-Based Android Malware Detection
Vikas Sihag, Gaurav Choudhary, Manu Vardhan, Pradeep Kumar Singh, Jung Taek Seo
Abstract
The post-COVID epidemic world has increased dependence on online businesses for day-to-day life transactions over the Internet, especially using the smartphone or handheld devices. This increased dependence has led to new attack surfaces which need to be evaluated by security researchers. The large market share of Android attracts malware authors to launch more sophisticated malware (12000 per day). The need to detect them is becoming crucial. Therefore, in this paper, we propose PICAndro that can enhance the accuracy and the depth of malware detection and categorization using packet inspection of captured network traffic. The identified network interactions are represented as images, which are fed in the CNN engine. It shows improved performance with the accuracy of 99.12% and 98.91% for malware detection and malware class detection, respectively, with high precision.