## 1 Introduction  
The mobile device has enormously penetrated society in the last few years as a result of enriched interactive features and cost-effectiveness. The advancement of sophisticated sensing devices has brought an exceptional boost to its employment in routine activities. The software developers have made efforts to develop multiple application software related to the academics, health, security, and entertainment sectors. A huge bulk of such application software is publicly available online to assist the end-users. Moreover, online available applications are freely downloaded and installed on user devices without spending any money. The most common operating systems for mobile devices are the Android and iOS operating systems. The software applications interoperable with these operating systems are available on the Google play store and also on third-party software platforms. The Android OS is the primary target for intruders to steal money and the privacy of the public. Malware is malicious software that attacks the operating system of the mobile and attacks the privacy of the users by stealing the credentials of the user. Malware software mostly attacks the android operating system whenever the user unknowingly installs unlicensed apps. The intrusion of malicious software variants through spyware, spoofing, hacking, and phishing has developed an alarming security thread for public credentials and private data. The phishing attacks deceive the recipient by persuading him to click the malicious URL leading to the installation of malware. The open-source code available contains malicious codes including trojan horses, riskware, spyware, adware, and ransomware [1,2]. The malware variants increase their production due to the use of intelligent malware-producingagents such as Zeus, SpyEyea and Dos [3; 4]. The privacy and security of the public have recently been researched to develop the privacy-aware applications [5], permissions-assisting application [6], and the malware detection frameworks [7; 8]. Anti-virus apps have been developed such as lookout, Norton, and Coodo mobile security, depending on signatures for the detection of malware. The malware signatures are the random snippets and binary patterns extracted from the data samples. The anti-virus developers use the cryptographic signature hash data. The signature data are stored initially to categorize the input data by providing the application [7]. However, malware can encounter the signatures-based detection mechanism by varying a subset of its software sections with preserved semantics. Various researchers have developed machine learning methods to detect malware in the Android operating system of mobile devices. The developed frameworks can broadly be classified into static and dynamic analysis methods. The static models decompile the application's codes in the installed package data. Moreover, the static features including the application program [9] interface, permission sequence [10], and metadata [11] are extracted in the recompilation process. The model is trained concerning the extracted set of static features for the robust classification of malicious activity by malware. The dynamic analysis models employ the reliable data of the applications, including the system calls [12] and traffic traces [13]. The malware identification model employs a dynamic set of features for the training and classification stage. The dimension of the static and dynamic feature set is extensive, and the model suffers from dimensionality issues. The details of features and samples of the OmniDroid dataset [14] are presented in Table 1. The irrelevant features increase the complexity of the malware detection framework; therefore, preprocessing methodologies have been developed to reduce the feature dimension by removing the unwanted feature values.