malware detection

Today, smartphones are prevalent for personal and corporate use and have become the new personal computer due to their portability, ease of use, and functionality (such as video conferencing, Internet browsing, e-mail, continuous wireless and data connectivity, worldwide map location services, and countless mobile applications such as banking applications). On the other hand, we store many sensitive and private information daily on smart devices. This information is of interest to malicious writers who are developing malware to steal information from mobile devices. Unfortunately, the open source and widespread adoption of the Android operating system has made it the most targeted of the four popular mobile platforms by malware writers. Many researchers have tried to identify malware using program signatures, which have been successful to some extent. However, the signature cannot effectively identify new and unknown malware. For this reason, in this article, we propose a method that designs a machine-learning model for Android malware detection based on the properties of Permissions, Intents APKs. In this study, we evaluated more than 25,000 Android samples belonging to malware and trusted samples. Experimental results show the effectiveness of the proposed method by obtaining 96.27% accuracy.

With the widespread using Internet in any device and services, several homes and workplace applications have been provided to avoid attacks. Connecting a system or device to an insecure network can create the possibility of being infected by unwanted files. Detecting such files is a vital task in any system. Employing machine learning (ML) is the most efficient method to detect these penetrations. On the other hand, malware programmers try to design malicious files that are hard to detect. A file can hide from detection in a feature view, but concealing in all views would be very difficult. In this paper, inspiring Multi-View Learning (MVL), we proposed to incorporate some various features such as Opcodes, Bytecodes, and System-calls to achieve complementary information to identify a file. In this way, we developed a modified version of Sparse Representation based Classifier (SRC) to aggregate the effect of all modalities in a unified classifier. To show the efficiency of the proposed method, we used several real datasets. Experimental results show the high performance of the proposed approach and its ability to cope with the imbalanced conditions.