android malware

Today, the mobile phone is one of the smart devices that have become a necessity in everyday life and are used for various tasks such as shopping, banking, communicating with friends, family, etc. In recent years, the Android operating system has been able to gain more popularity than other mobile phone operating systems. The number of software related to this operating system is also expanding at a remarkable speed. Unfortunately, this issue is not hidden from the profit-seeking people, and the production of malware of this operating system has also grown in parallel with its development. Third-party Android app stores that have emerged in recent years have become a very strong source of malware distribution, as these stores have weak to non-existent measures to prevent malicious apps from being uploaded and distributed to users' devices. Therefore, one of the challenges that programmers are dealing with in this field is to find solutions to establish security in these types of devices, in such a way that it provides powerful security analysis capabilities while consuming few resources on the device itself. Software products such as Lookout, Norton, and Comodo Mobile Security mainly use signature-based methods to detect malware threats. However, malware attackers use techniques such as repackaging and obfuscation to circumvent signatures and defeat attempts to analyze their internal mechanisms. The ever-increasing sophistication of Android malware requires new defense techniques that can protect users against new threats while not using up all of a mobile device's processing and storage resources. Therefore, in the current research, a computational offloading method is presented in the cloud structure to identify Android malware. The solution proposed by this research first extracts the features of Android applications during installation and execution on the mobile phone, then sends these extracted features to the cloud servers. On the cloud server side, these features are analyzed and using machine learning algorithms, malware is distinguished from clean programs. The proposed approach is trained and tested using the Drebin dataset. The obtained results show that the proposed approach has achieved 96.44% accuracy for malware detection.