Machine Learning Malware Detection For Android

• Main Author: Amir Muhammad Hafiz, Othman
• Format: Undergraduates Project Papers
• Language: English
• Published: 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://umpir.ump.edu.my/id/eprint/40874/1/CB20152.pdf; http://umpir.ump.edu.my/id/eprint/40874/

During this past year up until now, the total of malware that targets the Android operating system has increased compared to other operating systems. Therefore, an application that used the android operating system must be analyzed to detect the malware before the malware causes serious damage. To analyze the application, two types of analysis can be used which are static analysis and dynamic analysis. Static analysis is an analysis that is done by reviewing the codes diligently while dynamic analysis is an analysis that detects malware through observation. Even if both of the analysis was done successfully, however, an improvement needs to be done to detect the malware more accurately. With the technology arising now, there are many ways for the attacker to send malware to an Android smartphone user. Therefore, this research proposes a malware detection system using machine learning. This research objective is to detect the malware that attacks Android smartphones more accurately. The result of this research proves that the malware detection system is able to detect Android malware accurately.