# DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection
## 1 Introduction  
Smartphones have rapidly grown in popularity over the past decade, with billions of users, according to an analysis of Statista in 2021 [1]. The reason is that smartphones are so handy and convenient [2]. Sending emails, playing games, taking photographs and videos, searching the web, using GPS, and more are just some of the many uses for smartphones. Applications are being developed and improved daily, making it possible to achieve this, particularly on Android's operating system, which first appeared as a hacked Linux kernel optimized for touchscreen mobile gadgets. Moreover, last year Android OS apps extended to more than 3 million apps [3]. Banking, social media, healthcare, education, and entertainment are just some of the many possible uses for these Android apps [4]. As a result, most of these apps are employed to benefit their end consumers. Some of them, however, are used maliciously by hackers andexploiters. Malware refers to these harmful applications, defined as invasive software that steals data or causes damage to another user's computer [5]. Cybercriminals create malware to function in many ways, including adware, worms, ransomware, and Trojan viruses [6]. Because malicious software is always evolving, it is increasingly challenging to foil security breaches [7]. For instance, in 2021, Cybersecurity Check Point warned Android users that millions of mobile smartphones were vulnerable to Agent Smith malware [8]. The spyware also uses WhatsApp as a cover to attack Android systems. In 2021, coccus reported that more than a billion Android smartphones would be vulnerable to hacking because they lacked the latest security upgrades [9]. Additionally, experts from Kaspersky Lab in 2020 found that numerous hackers had used the Google Play app store to spread complex malware [10] for years. Recently, many Facebook accounts were hacked using the android malware \"FlyTrap\" app [11]. The Android operating system has a built-in authorization module that checks whether or not a security policy has been breached before granting the permissions requested by an Android app. The four categories of Android permissions correspond to the four levels of security outlined in [12]. Also, the dataset includes four types of malware that may be labeled as such: ransomware [13] adware [14], SMS malware, and scareware [15]. The ever-expanding and changing nature of malware has prompted numerous proposals for detecting and avoiding it. The research community has revealed two methods for spotting malware. Static, dynamic, and hybrid malware analysis are three types of analyzing android malware. Static malware analysis is where applications are inspected without being run, while dynamic analysis evaluates the behavior of malware in a sandbox after it has been run [16]. Despite the role of current technologies in improving quality of life and expanding the cyber world, cyber-threats have reached a new level and are increasing at a scary rate [17]. More importantly, new attacks that can breach a smartphone's defenses are constantly being developed and released. Violating the security policy might take various forms, depending on the mobile device's OS. This paper focuses on the Android operating system and new threats that threaten its security. The presence of malware in a mobile app has been investigated by several previous research [18; 19; 20; 21; 22], some of which made use of API calls and permissions. According to their findings, Dynamic analysis is also necessary since Static analysis is insufficient for detecting malware in obfuscated apps. It has been found in some research [23] that deep learning can be utilized to detect malware in mobile apps. This paper aims to provide a highly effective method for discovering and naming novel forms of malware, thus overcoming all these restrictions. This is why we have put time and effort into Android malware detection and family classification.