## 1 Introduction  
The impact of smartphones in our daily life is growing rapidly. It is obvious that these devices' popularity and usage rates have significantly risen because of the wide range of functions they can provide us. As of 2022, Statista [1] estimates that there will be 6.56 million mobile phone users worldwide. In fact, UN statisticians have [2] predicted that by October 2022, among a population of 8 billion, approximately 82% of the total population in the world own a smartphone. Android is the most widely used smartphone operating system, with a 71.85% market share as of July 2022, according to Statista statistics [3]. This results from Android's open-source nature and other characteristics. Given its open-source nature, it is inevitable that an operating system with such widespread use would attract malware [4]. Due to these circumstances, mobile malware has emerged. The term \"malware\" describes software applications that have been installed on a device with a variety of purposes, such as to annoy users, steal personal user data, consume system resources without permission, or harm the device [5]. After being installed on the target device, these programs operate to fulfill their intended functions, either visibly or invisibly to the user, in the background [6]. During the first two quarters of 2021, more than 2.2 million new instances of mobile malware were reported, according to Statista [7]. Given all of these facts, it is obvious that Android malware represents a significant threat to mobile systems. Today, it is widely known that smartphones contain personal data about users, including their banking details, confidential information, and health data. Therefore, it is evident that both the security and integrity of these sensitive data will be compromised if these smartphones are exposed to a malware. When malware is planted on a smartphone, it can acquire the user's bank account if the user is using online banking apps, as well as their health records and private, sensitive data such as personal information. Using the Android smartphone, the malware can send emails and SMS messages without the user's consent to spread the malware to even more users. It is inevitable that malware developers will target smartphones more frequently considering all of these capabilities. The fact that the frequency of mobile malware rose by approximately 1800% in 2016 is among the most obvious and concrete indicator of this problem [8]. These issues have led to a wide range of models being proposed for malware detection on Android mobile devices. In the past, methods based on signatures have emerged. These methods search the application code to detect known patterns of malicious code. Although signature-based approaches are currently effective in terms of their performance and in detecting malware that has already been identified, they cannot be used to detect new types of malware, and as a result, more modern techniques and models have replaced them. These models can be examined in three categories:  
* Static malware detection methods. * Dynamic malware detection methods. * Hybrid malware detection methods. All three of the aforementioned methods are frequently applied using machine-learning-based techniques. The ability of machine-learning-based approaches to detect previously unknown malware [9] makes them vital. Because static detection techniques evaluate software codes without running suspicious applications, they are unable to detect malicious programs that obfuscate the program code or employ encryption [10] to protect the malware. On the other hand, approaches based on dynamic detection methods run the application in a secure environment to search for any malicious behavior [11]. However, since the performance cost of the application rises in dynamic detection-based methods, it becomes challenging to perform a real-time analysis due to the extension of the execution time [12]. Both strategies have advantages and disadvantages in comparison with each other.